EP0948847A1 - Systeme de traitement de signaux dans le meme canal - Google Patents

Systeme de traitement de signaux dans le meme canal

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Publication number
EP0948847A1
EP0948847A1 EP96940872A EP96940872A EP0948847A1 EP 0948847 A1 EP0948847 A1 EP 0948847A1 EP 96940872 A EP96940872 A EP 96940872A EP 96940872 A EP96940872 A EP 96940872A EP 0948847 A1 EP0948847 A1 EP 0948847A1
Authority
EP
European Patent Office
Prior art keywords
signals
signal
received
generating
egsv
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP96940872A
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German (de)
English (en)
Inventor
Mithat Can Dog An
Stephen Deane Stearns
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northrop Grumman Corp
Original Assignee
TRW Inc
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Filing date
Publication date
Application filed by TRW Inc filed Critical TRW Inc
Publication of EP0948847A1 publication Critical patent/EP0948847A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming

Definitions

  • This invention relates generally to signal processing systems and, more particularly, to apparatus and methods for receiving and processing signals that share a common receiver frequency band at the same time, referred to as cochannel signals. Even two signals transmitted on slightly separated frequency bands may be "cochannel" signals as seen by a receiver operating to receive signals on a bandwidth that overlaps both of the signals. In a variety of signal processing applications, there is a need to recover information contained in such multiple, simultaneously received signals.
  • the word “recover” or “recovery” encompasses separation of the received signals, “copying" the signals (i.e., retrieving any information contained in them), and, in some applications, combining signals received over multiple paths from a single source.
  • the "signals" may be electromagnetic signals transmitted in the atmosphere or in space, acoustic signals transmitted through liquids or solids, or other types of signals characterized by a time- varying parameter, such as the amplitude of a wave.
  • signal processing includes transmission of cochannel signals.
  • signals are received by "sensors."
  • a sensor is an appropriately selected transducer for converting energy contained in the signal to a more easily manipulated form, such as electrical energy.
  • electromagnetic signals are received by antennas and converted to electrical signals for further processing. After separation of the signals, they may be forwarded separately to transducers of a different type, such as loudspeakers, for converting the separated electrical signals into audio signals.
  • the signal content may be of less importance than the directions from which the signals were received, and in other applications the received signals may not be amenable to conversion to audible form. Instead, each recovered signal may contain information in digital form, or may contain information that is best understood by displaying it on a chart or electronic display device.
  • the present invention is characterized by multiple signals received by sensors simultaneously at the same or overlapping frequencies, the need to separate, recover, identify or combine the signals and, optionally, some type of output transducer to put the recovered information in a more easily discernible form.
  • Separation and recovery of signals of different frequencies is a routine matter and is handled by appropriate filtering of the received signals. It is common knowledge that television and radio signals are transmitted on different frequency bands and that one may select a desired signal by tuning a receiver to a specific channel. Separation and recovery of multiple signals transmitted at different frequencies and received simultaneously may be effected by similar means, using multiple tuned receivers in parallel. A more difficult problem, and the one with which the present invention is concerned, is how to separate and copy signals from multiple sources when the transmitted signals are at the same or overlapping frequencies. A single sensor, such as an antenna, is unable to distinguish between two or more received signals at the same frequency. However, antenna array technology provides for the separation of signals received from different directions.
  • an antenna array can be electronically “steered” to transmit or receive signals to or from a desired direction. Moreover, the characteristics of the antenna array can be selectively modified to present "nulls" in the directions of signals other than that of the signal of interest.
  • a further development in the processing of array signals was the addition of a control system to steer the array toward a signal of interest. This feature is called adaptive array processing and has been known for at least two to three decades. See, for example, a paper by B. Widrow, P.E. Mantey, L.J. Griffiths and B.B. Goode, "Adaptive Antenna Systems," Proceedings of the IEEE, vol. 55, no. 12, pp. 2143-2159, December 1967.
  • the steering characteristics of the antenna can be rapidly switched to receive signals from multiple directions in a "time-sliced" manner. At one instant the antenna array is receiving a signal from one source and at the next instant, from a different source in a different direction, but information from the multiple sources is sampled rapidly enough to provide a complete record of all the received signals. It will be understood that, although steered antenna array technology was developed principally in the communications and radar fields, it is also applicable to the separation of acoustic and other types of signals.
  • a communication signal typically includes a carrier signal at a selected frequency, on which is impressed or modulated an information signal.
  • modulation schemes including amplitude modulation, in which the amplitude of the signal is varied in accordance with the value of an information signal, while the frequency stays constant, and frequency or phase modulation, in which the amplitude of the signal stays constant while its frequency or phase is varied to encode the information signal onto the carrier.
  • amplitude modulation in which the amplitude of the signal is varied in accordance with the value of an information signal, while the frequency stays constant
  • frequency or phase modulation in which the amplitude of the signal stays constant while its frequency or phase is varied to encode the information signal onto the carrier.
  • Various forms of frequency and phase modulation are often referred to as constant modulus modulation methods, because the amplitude or modulus of the signal remains constant, at least in theory.
  • the modulus is subject to distortion during transmission, and various devices, such as adaptive equalizers, are used to restore the constant-modulus characteristic of the signal at a receiver.
  • the constant modulus algorithm was developed for this purpose and later applied to antenna arrays in a process called adaptive beam forming The following references are provided by way for further background on the constant modulus algorithm:
  • the constant modulus algorithm works satisfactorily only for constant modulus signals, such as frequency-modulated (FM) signals or various forms of phase- shift keying (PSK) in which the phase is discretely or continuously varied to represent an information signal, but not for amplitude-modulated (AM) signals or modulation schemes that employ a combination of amplitude and phase modulation.
  • modulation schemes used known as M-ary quadrature amplitude modulation (QAM), used for transmitting digital data, whereby the instantaneous phase and amplitude of the carrier signal represents a selected data state.
  • M-ary quadrature amplitude modulation used for transmitting digital data, whereby the instantaneous phase and amplitude of the carrier signal represents a selected data state.
  • 16-ary QAM has sixteen distinct phase-amplitude combinations.
  • the "signal constellation" diagram for such a scheme has sixteen points arranged in a square matrix and lying on three separate constant-modulus circles.
  • a signal constellation diagram is a convenient way of depicting all the possible signal states of a digitally modulated signal. In such a diagram, phase is represented by angular position and modulus is represented by distance from an origin.
  • the constant modulus algorithm has been applied with limited success to a 16-ary QAM scheme, because it can be represented as three separate constant- modulus signal constellations.
  • the constant modulus algorithm provides rapidly decreasing accuracy.
  • the constant modulus approach fails completely in trying to recover cochannel AM signals. If there are multiple signals, the constant modulus algorithm yields signals with "cross-talk," i.e. with information in the two signals being confused. For a single AM signal in the presence of noise, the constant-modulus algorithm yields a relatively noisy signal.
  • antenna arrays can be steered electronically to determine the directions of signal sources, it was perhaps not surprising that one well known form signal separator available prior to the present invention used direction finding as its basis.
  • the approach is referred to as DF-aided copy, where DF means direction finding.
  • DF means direction finding.
  • This is an open-loop technique in which steering vectors that correspond to estimated signal source bearings are first determined; then used to extract waveforms of received signals.
  • the direction finding phase of this approach requires a knowledge of the geometry and performance characteristics of the antenna array. Then steering vectors are fed forward to a beamformer, which nulls out the unwanted signals and steers one or more antenna beam(s) toward each selected source.
  • DF direction-finding
  • Such systems separate cochannel signals by means of a multi-source (or cochannel) super-resolution direction finding algorithm that determines steering vectors and directions of arrival (DOAs) of multiple simultaneously detected cochannel signal sources.
  • DOAs directions of arrival
  • An algorithm determines beam- forming weight vectors from the set of steering vectors of the detected signals. The beamforming weight vectors are then used to recover the signals.
  • Any of several well- known multi-source super-resolution DF algorithms can be used in such a system.
  • MUSIC Multiple Signal Classification
  • ESPRIT Estimat of Signal Parameters via Rotational Invariance Techniques
  • WSF Weighted Subspace Fitting
  • MODE Method of Direction Estimation
  • MUSIC was developed in 1979 simultaneously by Ralph Schmidt in the United States and by Georges Bienvenu and Lawrence Kopp in France.
  • the Schmidt work is described in R.O. Schmidt, "Multiple emitter location and signal parameter estimation," Proc. RADC Spectrum Estimation Workshop, pp. 243-258, Rome Air Development Center, Griffiss Air Force Base, NY, October 3-5, 1979.
  • the Bienvenu work is described in G. Bienvenu and L. Kopp, "Principe de la goniometrie passive adaptative," Proc. Colloque GRETSI, pp. 106/1-106/10, Nice, France, May 1979.
  • MUSIC has been extensively studied and is the standard against which other super- resolution DF algorithms are compared.
  • ESPRIT calculates two N-by-N covariance matrices, where N is the number of antenna elements, and solves a generalized eigenvalue problem numerically (instead of using a calibration table search, as MUSIC does). It does this for every block of input samples.
  • MUSIC calculates a single N-by-N covariance matrix, performs an eigendecomposition, and searches a calibration table on every block of input array samples (snapshots).
  • MUSIC and ESPRIT have a number of shortcomings, some of which are discussed in the following paragraphs.
  • ESPRIT was successfully marketed based on a single, key advantage over MUSIC. Unlike MUSIC, ESPRIT did not require array calibration. In ESPRIT, the array calibration requirement was eliminated, and a different requirement on the antenna array was substituted. The new requirement was that the array must have a certain geometrical property. Specifically the array must consist of two identical sub- arrays, one of which is offset from the other by a known displacement vector. In addition, ESPRIT makes the assumption that the phases of received signals at one sub- array are related to the phases at the other sub-array in an ideal theoretical way.
  • Array manifold is a term used in antenna design to refer to a multiplicity of physical antenna parameters that, broadly speaking, define the performance characteristics of the array.
  • a well known difficulty with communication systems is that signals from a single source may be received over multiple paths that include reflections from buildings and other objects.
  • the multiple paths may interpose different time delays, phase changes and amplitude changes on the transmitted signals, rendering reception more difficult, and transmission uncertain.
  • This difficulty is referred to as the multipath problem. It is one that has not been adequately addressed by signal processing systems of the prior art.
  • MUSIC nor ESPRIT can operate in a coherent multipath environment without major added complexity.
  • a related problem is that, in a signal environment devoid of coherent multipath, no DF-beamforming method can separate signals from sources that are collinear with the receiving array, i.e. signal sources that are in line with the array and have zero angular separation. Even in a coherent multipath environment, DF-beamforming methods like MUSIC and ESPRIT cannot separate and recover cochannel signals from collinear sources.
  • ESPRIT requires two antenna sub- arrays and is highly sensitive to mechanical positioning of the two sub-arrays, and to the electromagnetic matching of each antenna in one sub-array with its counterpart in the other sub-array. Also ESPRIT requires a 2N-channel receiver, where N is the number of antenna elements, and is highly sensitive to channel matching.
  • MUSIC and ESPRIT fail abruptly when the number of signals detected exceeds the capacity, N, equal to the number of antennas in the case of MUSIC, or half the number of antennas in the case of ESPRIT.
  • N the capacity
  • a fundamental problem with both MUSIC and ESPRIT is that they use open-loop feed-forward computations, in which errors in the determined steering vectors are uncorrected, uncorrectable, and propagate into subsequent calculations.
  • MUSIC and ESPRIT have poorcross-talk rejection, as measured by signal-to-interference-plus-noise ratio (SINR) at the signal recovery output ports.
  • SINR signal-to-interference-plus-noise ratio
  • ESPRIT is best suited to ground based systems where its antenna requirements are best met and significant computational resources are available.
  • MUSIC has simpler antenna array requirements and lends itself to a wider range of platforms, but also needs significant computational resources.
  • Another limitation of most signal recovery systems of the prior art is that they rely on first-order and second-order statistical moments of the received signal data.
  • a moment is simply a statistical quantity derived from the original data by mathematical processing at some level.
  • An average or mean value of the several signals received at a given time is an example of a first-order moment.
  • the average of the squares of the signal values is an example of a second- order moment.
  • J.-F. Cardoso "Super-symmetric decomposition of the fourth-order cumulant tensor: blind identification of more sources than sensors," Proc. ICASSP-91, pp. 3109-3112, Toronto, Canada, May 1991. J.-F. Cardoso, "Higher-order narrowband array processing,” International Signal Processing Workshop on Higher Order Statistics, pp. 121—130, Chamrousse-France, July 10-12, 1991.
  • a system for receiving and processing multiple cochannel signals should make use of statistics of the measurements, and should not need to rely on knowledge of the geometry or array manifold of the sensors, i.e., the array calibration data. Also, the system should be able to receive and process cochannel signals regardless of their modulation or signal type, e.g. it should not be limited to constant- modulus signals. More generally, the ideal cochannel signal processing system should not be limited to any modulation properties, such as baud rate or exact center frequency. Any system that is limited by these properties has only a limited range of source types that can be separated, and is more suitable for interference suppression in situations where the desired signal properties are well known.
  • Another desirable property of the ideal cochannel signal receiving and processing system is that it should operate in a dynamic way, identifying new signal sources that appear and identifying sources that disappear. Another desirable characteristic is a very high speed of operation allowing received signals to be processed in real time. As will shortly become apparent, the present invention meets and exceeds these ideal characteristics for cochannel signal processing.
  • the present invention resides in a system or method for processing cochannel signals received at a sensor array and producing desired recovered signals or parameters as outputs.
  • cochannel signals are that overlap in frequency, as viewed from a receiver of the signals. Even signals that are transmitted in separate, but closely spaced, frequency bands may be cochannel signals as viewed from a receiver operating in bandwidth wide enough to overlap both of the signals.
  • a key aspect of the invention is that it is capable of separating and recovering multiple cochannel signals very rapidly using only sensor array signals, without knowledge of sensor array geometry and array manifold, (e.g. array calibration data), and without regard to the signal type or modulation. If array calibration data are available, the system also provides direction-of-arrival parameters for each signal source.
  • the invention inherently combines coherent multipath components of a received signal and as a result achieves improved performance in the presence of multipath.
  • One embodiment of the invention also includes a transmitter, which makes use of estimated generalized steering vectors generated while separating and recovering received signals, in order to generate appropriate steering vectors for transmitted signals, to ensure that transmitted signals intended for a particular signal source traverse generally the same path or paths that were followed by signals received from the same signal source.
  • the system of the invention comprises a signal receiving system, including means for generating a set of conditioned receiver signals from cochannel signals of any modulation or type received at a sensor array from multiple sources that can vary in power and location; an estimated generalized steering vector (EGSV) generator, for computing for each source an EGSV that results in optimization of a utility function that depends on fourth or higher even-order statistical cumulants derived from the received signals, the EGSV being indicative of a combination of signals received at the sensors from a signal source; and a supplemental computation module, for deriving output quantities of interest from the conditioned receiver signals and the EGSVs.
  • EGSV estimated generalized steering vector
  • the system includes a linear combiner, for repeatedly computing a single channel combined signal from the conditioned receiver signals and an EGSV; means for supplying an initial EGSV to the linear combiner, to produce the initial output of a single channel combined signal; an EGSV computation module, for computing successive values of the EGSV from successive values of the single channel combined signal received from the linear combiner and the conditioned receiver signals; and means for feeding the successive values of the EGSV back to the linear combiner for successive iteration cycles. Also included is means for terminating iterative operation upon convergence of the EGSV to a sufficiently accurate value.
  • the system includes a cross- cumulant matrix computation module, for generating a matrix of cross-cumulants of all combinations of the conditioned receiver signals; a structured quadratic form computation module, for computing successive cumulant strength functions derived from successive EGSVs and the cross-cumulant matrix; means for supplying an initial EGSV to the structured quadratic form computation module, to produce the initial output of a cumulant strength function; an ESGV computation module, for generating successive EGSVs from successive cumulant strength functions received from the structured quadratic form computation module; means for feeding the successive values of the EGSV back to the structured quadratic form computation module for successive iteration cycles; and means for terminating iterative operation upon convergence of the EGSV to a sufficiently accurate value.
  • a cross- cumulant matrix computation module for generating a matrix of cross-cumulants of all combinations of the conditioned receiver signals
  • a structured quadratic form computation module for computing successive cumulant strength functions derived from successive EGSVs and the cross-cumul
  • the system includes a cross-cumulant matrix computation module, for generating a matrix of cross-cumulants of all combinations of the conditioned receiver signals; and an EGSV computation module for computing the EGSV directly from the cross-cumulant matrix by solving a fourth degree polynomial equation.
  • signal processing may employ one of several different cumulant recovery (CURE) techniques.
  • the means for generating the set of conditioned signals includes a covariance matrix computation module, an eigendecomposition module for generating the eigenstructure of the covariance matrix and an estimate of the number of signal sources, and a transformation matrix for conditioning the receiver signals.
  • An EGSV generator then employs signals output by the eigendecomposition module to compute EGSVs. This technique is referred to in this specification as the eigenCURE or eCURE system.
  • the means for generating the set of conditioned signals includes a covariance matrix computation module and a matrix decomposition module, for generating the inverse covariance matrix and a transformation matrix for conditioning the receiver signals.
  • An EGSV generator then employs signals output by the eigendecomposition module to compute EGSVs.
  • the system further includes a beamformer, for generating a recovered signal from the receiver signals by using the EGSV(s) and the matrix obtained from the matrix decomposition module.
  • the means for generating the set of conditioned signals includes a covariance matrix computation module, an eigendecomposition module for generating an estimate of the number of signal sources, a transformation matrix for conditioning the receiver signals, and an eigenstructure derived from the receiver signals.
  • the EGSV generator employs signals output by the eigendecomposition module to compute EGSVs. Processing is on a block-by -block basis, and the system further comprises a multiple port signal recovery unit, including means for matching current EGSVs with EGSVs from a prior data block to impose waveform continuity from block to block.
  • the initial values of EGSVs for each new processing block are computed by combining a prior block EGSV and a cumulant vector derived from the utility function used in the EGSV generator. More specifically, the means for combining takes the sum of the prior block EGSV multiplied by a first factor, and the cumulant vector multiplied by a second factor. The first and second factors may be selected to provide an initial EGSV that anticipates and compensates for movement of a signal source.
  • the system functions to separate a plurality (P) of received cochannel signals. If the first iterative method is employed, there are multiple EGSV generators (P in number), including P EGSV computation modules and P linear combiners, for generating an equal plurality (P) of EGSVs associated with P signal sources.
  • the supplemental computation module functions to recover P separate received signals from the P generalized steering vectors and the conditioned receiver signals.
  • the supplemental computation module includes a recovery beamformer weight vector computation module, for generating from all of the EGSVs a plurality (P) of receive weight vectors, and a plurality (P) of recovery beamformers, each coupled to receive one of the P receive weight vectors and the conditioned receiver signals, for generating a plurality (P) of recovered signals.
  • a recovery beamformer weight vector computation module for generating from all of the EGSVs a plurality (P) of receive weight vectors, and a plurality (P) of recovery beamformers, each coupled to receive one of the P receive weight vectors and the conditioned receiver signals, for generating a plurality (P) of recovered signals.
  • the supplemental computation module includes a recovery beamformer weight vector computation module, for generating from all of the EGSVs a plurality (P) of receive weight vectors, and a plurality (P) of recovery beamformers, each coupled to receive one of the P receive weight vectors and the conditioned receiver signals, for generating a plurality (P) of recovered signals.
  • the ESGV computation module If the direct processing method is used to separate two signals, the ESGV computation module generates two EGSVs from the cross-cumulant matrix data; and the supplemental computation module functions to recover two separate received signals from the two generalized steering vectors and the conditioned receiver signals.
  • the supplemental computation module includes a recovery beamformer weight vector computation module, for generating from both of the EGSVs two receive weight vectors, and two recovery beamformers, each coupled to receive one of the receive weight vectors and the conditioned receiver signals, for generating two recovered signals.
  • the system of the invention functions to derive the direction of arrival (DOA) of a received signal; and the supplemental computation module includes a memory for storing sensor array calibration data, and means for deriving the DOA of a received signal from its associated steering vector and the stored sensor array calibration data.
  • the sensor array calibration data includes a table associating multiple DOA values with corresponding steering vectors; and the means for deriving the DOA includes means for performing a reverse table lookup function to obtain an approximated DOA value from a steering vector supplied by the generalized steering vector generator.
  • the means for deriving the DOA may also include means for interpolating between two DOA values to obtain a more accurate result.
  • the supplemental computation module of the signal processing system also includes a transmitter, for generating transmit signal beamformer weights from the received signal beamformer weights, and for transmitting signals containing information in a direction determined by the transmit signal beamformer weights.
  • CURE basic cumulant recovery
  • An important application of the invention is in two-way radio communication. Because CURE processing generates an estimated generalized steering vector not necessarily for each received signal, but for each signal source, the invention provides an important benefit when used in multipath conditions. Signals reaching a receiving antenna array over multiple paths will be combined in the CURE system if the received components are coherent, and the resultant generalized steering vector represents the combined effect of all the coherent multipath signals received at the antenna. This feature has a number of advantages. First, a radio receiving system using the CURE system is inherently immune to multipath problems encountered by conventional receivers.
  • the antenna directivity pattern there can be an associated generalized null in the antenna directivity pattern, which can be used to null out an interfering signal having multipath structure in favor of a cochannel signal from another source.
  • the signal recovery method provides a diversity gain in the presence of multipath components, such that a stronger combined signal is received as compared with a system that discards all but one component.
  • the generalized steering vector concept allows multiple cochannel signals to be received and transmitted in the presence of multipath effects. Fifth, cochannel signal sources that are collinear with the receiver sensor array can be received and separated if there are multipath components.
  • the CURE signal separation system can be used to separate signals transmitted in different modes over a "waveguide,” by which is meant any bounded propagation medium, such as a microwave waveguide, an optical waveguide, a coaxial cable, or even a twisted pair of conductors.
  • a microwave waveguide such as a microwave waveguide, an optical waveguide, a coaxial cable, or even a twisted pair of conductors.
  • the transmission modes may become mixed in the waveguide, the original signals are easily recovered in the CURE system.
  • the CURE signal separation system can be used to separate signals recorded on closely space tracks on a recording medium. Crosstalk between the signals on adjacent tracks is eliminated by using the
  • the CURE signal separation system can be used to extend the effective dynamic range of a receiver system.
  • the CURE signal separation system can be used to perform a complex phase equalization function automatically, without knowledge of the amount of phase correction that is needed.
  • the CURE system may be modified to compensate for moving signal sources, and may also be modified to handle a wideband signal separation problem.
  • the wideband signal separator includes multiple narrowband CURE systems, means for decomposing a wide band of signals into multiple narrowbands for processing, and means for combining the narrow bands again.
  • FIG. 1 is a taxonomy diagram depicting the interrelationships of the invention's various cochannel signal processing methods.
  • FIG. 2 A is a block diagram of the cumulant recovery (CURE) system of the invention in general form, depicting a first iterative method.
  • CURE cumulant recovery
  • FIG. 2B is a block diagram similar to FIG. 2A, but depicting a second iterative method.
  • FIG. 2C is a block diagram similar to FIG. 2A, depicting a direct computation method.
  • FIG. 3 A is a block diagram similar to FIG. 2A, but modified to depict how the invention functions to separate multiple received cochannel signals.
  • FIG. 3B is a block diagram similar to FIG. 3A, but depicting the first iterative method.
  • FIG. 3C is a block diagram similar to FIG. 3A, but depicting the direct computation method.
  • FIG. 4A is a block diagram of a supplemental computation module for signal recovery, shown in FIGS. 3A, 3B and 3C.
  • FIG. 4B is a block diagram of a supplemental computation module for use in conjunction with the system of FIGS. 2A, 2B or 2C, to provide a direction finding function.
  • FIG. 5 is a block diagram similar to FIG. 2A, but modified to depict a receiver/ transmitter function.
  • FIG. 6 is a block diagram similar to FIG. 3A, but modified to show input of signals from a signal "waveguide. "
  • FIG. 7 is a block diagram showing in more detail the functions performed in accordance with the two iterative methods for cochannel signal separation and .recovery.
  • FIG. 8 A is simplified block diagram of a cochannel signal recovery system in accordance with the present invention.
  • FIG. 8B is a hardware block diagram similar to FIG. 8A, of one preferred embodiment of the invention.
  • FIG. 9 (comprising FIGS. 9 A and 9B) is another block diagram of the system of FIG. 7, with some of the subsystem functions recited in each of the blocks.
  • FIG. 10 is a block diagram of the preprocessor computer of FIG. 7.
  • FIG. 11 (comprising FIGS. 11A and 11B) is a block diagram of a signal extraction port in an active state.
  • FIG. 12 is a block diagram of the multiple port recovery unit of FIG. 9, including multiple signal extraction ports and an orthogonalizer.
  • FIG. 13 (comprising FIGS. 13A, 13B and 13C) is a block diagram of the signal recovery controller of FIG. 7.
  • FIG. 14 is a block diagram of a signal extraction port in the inactive state.
  • FIG. 15 is a block diagram of signal recovery system using covariance inversion cumulant recovery (CiCURE).
  • FIG. 16 is a block diagram of a signal extraction port in the active state using the CiCURE system illustrated in FIG. 15.
  • FIG. 17 is a block diagram of an alternate embodiment of the invention referred to as pipeCURE.
  • FIG. 18 is a schematic diagram depicting a processing difficulty associated with moving signal sources.
  • FIGS. 19 and 20 are vector diagrams depicting the effect of using ⁇ - ⁇ CURE and ⁇ CURE updating to initialize a block of samples for eCURE processing in the moving source situation such as shown in FIG. 18.
  • FIG. 21 is a schematic diagram showing an overload condition in which there are more signal sources that antenna elements.
  • FIG. 22 is a diagram of an antenna array directivity pattern for handling the situation shown in FIG. 21.
  • FIG. 23 is a schematic diagram depicting multipath propagation paths from a transmitter to a receiver array.
  • FIG. 24 is a schematic diagram depicting how the system of the invention handles coherent multipath signals in the presence of interference.
  • FIG. 25 is a schematic diagram depicting how the system of the invention handles non-coherent multipath signals in the presence of interference.
  • FIG. 26 is a schematic diagram depicting how the system of the invention handles receiving a desired signal in the presence of an interference signal with multipath components and a second interference source.
  • FIG. 27 is a schematic diagram depicting the invention as used in the recovery of a received signal in the presence of a strong local transmitter.
  • FIG. 28 is a schematic diagram depicting the invention as used in the recovery of a weak received signal in the presence of a strong jamming signal located nearby.
  • FIG. 29 is a schematic diagram depicting portions of a cellular telephone communication system of the prior art.
  • FIG. 30 is a schematic diagram depicting how a communication system of the prior art separates cochannel signals.
  • FIG. 31 is a schematic diagram depicting how the present invention operates in the environment of a cellular telephone communication system.
  • FIG. 32 is a block diagram showing major portions of a transmitter as used in conjunction with the system of the present invention.
  • FIG. 33 is a block diagram showing a transmitter similar to the one in FIG. 32, but with more detail of some aspects of the apparatus.
  • FIGS. 34A and 34B are diagrams depicting operation of the invention in recovery of dual-polarized signals.
  • FIG. 35 is a schematic diagram depicting the invention as used in conjunction with an optical fiber network.
  • FIG. 36 is a schematic diagram depicting the invention as used in copy- aided direction finding.
  • FIG. 37 is a schematic diagram depicting the invention as used in extending the dynamic range of a receiver.
  • FIG. 38 is a schematic diagram depicting the invention as used in disk recording system.
  • FIG. 39 is a schematic diagram depicting the invention as used to effect automatic phase rotation equalization of a QAM signal.
  • FIG. 40A is a signal constellation diagram for a QAM system with four amplitude-phase states by way of illustration.
  • FIG. 40B is a diagram similar to FIG. 40A, showing a phase rotation of ⁇ as a result of propagation of the signal to the receiver.
  • FIG. 41 is a block diagram showing extension of the cumulant recovery system of the invention to cover a wide frequency band. DESCRIPTION OF THE PREFERRED EMBODIMENTS
  • the present invention resides in a system and method for processing signals received by a sensor receiving system having an array of sensor elements.
  • the system is capable of receiving and processing a signal from a single source, and for purposes of explanation the system will sometimes be described as receiving and processing just one signal. It will, however, shortly become clear that the system is capable of, and best suited for, receiving and processing signals from multiple sources, and that the multiple signals may utilize overlapping signal frequency bands as viewed at the receiver, i.e. they may be cochannel signals.
  • detection and processing of cochannel signals presents difficulties that have not been adequately addressed by signal processing systems of the prior art.
  • One central concept that makes the various aspects of overall invention possible is the ability to determine accurate estimates of generalized steering vectors of the source signals incident on a sensor array or, more generally, estimates of the linear combinations that represent the relative amplitudes of the source signals in each channel of a multichannel signal stream.
  • the meaning of the term "generalized steering vector" will become better understood as this description proceeds.
  • a definition for present purposes is that the generalized steering vector associated with a particular signal source represents the weighted sum of ordinary steering vectors corresponding to all multipath components of signals from that source.
  • An ordinary steering vector is the value of the array manifold at a single angle corresponding to a source's DOA. Multipath components arise when a signal from a single source reaches a receiver over multiple propagation paths.
  • the invention encompasses three distinct methods for determining estimates of the generalized steering vectors of source signals.
  • FIG. 1 illustrates the taxonomy and relationships among the methods.
  • Two of the methods for determining the estimated generalized steering vectors (EGSVs) involve iterative computations. The first of these, indicated by reference numeral 10, is called the "beamform - cross- cumulant" method.
  • an initial EGSV is iteratively updated and improved by a method that involves using the initial EGSV as a complex weight vector in a linear beamformer that operates on sensor signals (i.e. , signal measured at a sensor array, to be discussed shortly with reference to other drawing figures).
  • Cross- cumulants are computed, specifically between the output signal from the beamformer used in this method and each of the beamformer 's input signals.
  • cumulant is defined more completely in later sections of this specification, but for purposes of this general discussion it is sufficient to note that cumulants are fourth-order (or higher even-order) statistical moments of the received signals.
  • the cross-cumulants are formed into a vector which, upon normalization, becomes the next EGSV. This iteration cycle is repeated until convergence is attained, based on a predefined convergence test or on a selected number of iteration cycles. Upon convergence, the EGSV will have converged to the generalized steering vector of one of the incident source signals.
  • the second iterative method for obtaining EGSVs is referred to as the "C- matrix” method and is indicated at 12 in FIG. 1.
  • C- matrix The second iterative method for obtaining EGSVs.
  • a particular matrix of cross-cumulants among the sensor signals is calculated first, before the iterative process starts. There are no subsequent calculations of cumulants within the iterative cycle. Instead, an initial EGSV is used to calculate a structured quadratic form, which yields a quantity called "cumulant strength.” The EGSV is adjusted to maximize the cumulant strength by means of an iterative optimization procedure. Upon convergence, the EGSV will have converged to the generalized steering vector of one of the incident source signals.
  • the third method of finding the EGSVs is non-iterative. It is referred to as the "direct computation” or “analytic” method and is indicated at 14 in FIG. 1. This method can only recoverup to two incident signals, but is unrestricted as to the number of sensor array elements or processing channels.
  • the method first computes the same matrix of cross-cumulants among the sensor signals as is used in the second iterative method. However, a fourth degree polynomial equation is solved instead of optimizing a structured quadratic form. The solutions to the fourth degree polynomial yield the final EGSVs directly without iteration.
  • FIG. 1 shows diagrammatically that the EGSVs generated by either of the three methods (10, 12 or 14) are coupled over line 16 to a selected one of three algorithmic structures 18, referred to as CiCURE, eCURE, and pipeCURE.
  • CiCURE stands for cumulant recovery.
  • the terms CiCURE, eCURE and pipeCURE refer to specific embodiments of the invention known as covariance inversion CURE, eigenCURE and pipelined CURE, respectively.
  • Each structure encompassed by the block 18 in FIG. 1 can employ any of the three methods (10, 12 or 14) described above for determining the source signal EGSVs.
  • the algorithmic structures (18) are described below in subsequent descriptive sections as processing input samples in a "batch processing" mode, meaning that sampled data from the sensor signals are grouped into blocks of samples, which are then processed one block at a time.
  • the block size is arbitrary and is not intended to limit the scope of the present invention.
  • the block size can be as small as one, in which case, the batch mode reduces trivially to "sample-by-sample” processing.
  • the EGSVs of the source signals vary with time, due, for example, to changing geometric relationships among the locations of the sources, the receiving sensor array, or multipath reflectors. For such situations, two methods are presented for determining the initial EGSVs for each block of samples.
  • FIG. 2A shows the system of the present invention in general form, employing the first iterative method referred to above as the beamform - cross- cumulant method.
  • the system includes four principal components: a sensor receiving system 20, an estimated generalized steering vector (EGSV) computation module 22, a supplemental computation module 24 and a linear combiner 26.
  • the sensor receiving system 20 includes components not shown in
  • incident signals 27 are sensed by the sensor receiving system 20 and, after appropriate processing, the supplemental processing module 24 generates desired output signals or parameters as needed for a specific application of the invention.
  • the output signals will be the reconstructed cochannel signals received from separate signal sources.
  • the received signals may be of any modulation or type.
  • a key component of the signal processing system of the invention is the estimated generalized steering vector (EGSV) computation module 22, which receives the conditioned received signals over line 28 from the sensor receiving system 20, and computes an EGSV from the conditioned signals.
  • the EGSV in accordance with the invention, results in optimization of a utility function that depends on fourth or higher even-order statistical cumulants derived from the received signals. At this point in the description, cumulants have not yet been defined, but they are discussed in Section 4.0. For the present, it is sufficient to note that cumulants are fourth-order (or higher even- order) statistical moments of the received signals.
  • the EGSV computation module 22 functions in an iterative manner in cooperation with the linear combiner 26.
  • the linear combiner 26 begins a first iteration with an initial EGSV, indicated at 30, which may be an estimate or simply a random initial vector quantity.
  • the linear combiner 26 then combines the received signals on line 28 with the initial EGSV and generates a single-channel combined signal on line 32 to the EGSV computation module 22.
  • the latter performs its cumulant computation and generates a revised EGSV, which is fed back to the linear combiner 26 over line 34.
  • the iterative process continues until the EGSV has converged to an appropriately accurate solution, which is output to the supplemental computation module 24 over line 36.
  • the output on line 36 is also referred to as an estimated generalized steering vector, which closely approximates the actual generalized steering vector and represents the weighted sum of ordinary steering vectors corresponding to all multipath components of signals from a source. Where no multipath components are present, the generalized steering vector reduces to an ordinary steering vector for the source.
  • the term "generalized" steering vector is used frequently in this specification, to convey the meaning that the steering vector generated automatically and dynamically takes into account the possibility of receiving a signal over multiple paths.
  • the function of the supplemental computation module 24 depends on the specific application of the invention. This module makes use of the conditioned received signals on line 28 and the generalized steering vector on line 36, and generates the desired output signal or parameters on output line 38.
  • the components shown in FIG. 2 A and in particular the EGSV computation module 22 and the linear combiner 26, function to generate a single estimated generalized steering vector on line 36, corresponding to a single signal source from which signals are received at the sensor receiving system 20.
  • multiple linear combiners 26 and multiple EGSV computation modules 22 will be needed to separate signals from multiple sources.
  • FIG. 2 A Another aspect of FIG. 2 A to keep in mind is that it is an imperfect attempt to depict a number of the invention's many possible forms in a single conceptual diagram.
  • the concept of the generalized steering vector is central to all forms of the invention.
  • the estimated generalized steering vector (EGSV) provides a means for tracking sources through time free of discontinuity.
  • the estimated generalized steering vector computational module, 22 in FIG. 2A may be partially merged with the supplemental computation module 24 or the order of computation modified to accommodate physical implementation. This is true in the case of a specific implementation of a signal recovery system, described subsequently, in which recovered signals are generated as outputs of the supplemental computation module 24.
  • each EGSV represents the weighted sum of steering vectors associated with multipath components derived from a single signal source. As will be described in the next descriptive subsection, multiple EGSVs can be conveniently processed to reconstruct and separate signals from multiple signal sources.
  • the common components include the EGSV computation module 22, the linear combiner 26 and signal conditioning portions of the sensor receiving system 20. Accordingly, in many instances it would be advantageous to fabricate these components as a monolithic semiconductor device or chip, to be mounted in close association with other components in the supplemental computation module 24, which vary by application.
  • a set of semiconductor chips could include various embodiments of the supplemental computation module 24, such as for signal separation, direction finding and so forth.
  • FIG. 2B illustrates the form of the invention that uses the second iterative method (12, FIG. 1), called the C-matrix method, for generating EGSVs. It will be observed that the figure is similar to FIG. 2 A, except that there is no linear combiner. Instead there is a cross-cumulant matrix computation module 40 and a structured quadratic form computation module 42.
  • the cross-cumulant computation module 40 receives conditioned sensor signals over line 28 from the sensor receiving system 20, computes the cross-cumulants of all combinations of the sensor signals and stores the results in a matrix having a particular mathematical structure.
  • This matrix of cross-cumulants denoted C
  • P is the number of signal sources, and is stored for subsequent computations.
  • This C matrix is computed before any iterative computations are performed, and it will be noted that the EGSVs fed back from the EGSV computation module 22 over line 34 are coupled to the structured quadratic form computation module 42 and not to the cross-cumulant matrix computation module 40.
  • the structured quadratic form computation module 42 is used in an iterative computational loop that, starting with an initial estimated generalized steering vector (EGSV) on line 30, produces a series of successively improved values for the EGSV, until a termination test is satisfied.
  • the module 42 receives an input EGSV and outputs a cumulant strength function (on line 32), which is obtained by computing a structured quadratic form involving the C matrix and the input EGSV.
  • the mathematical details of the computation are described in a later section.
  • the cumulant strength function, so obtained, is output to the EGSV computation module 22 to update the EGSV.
  • the nature of the supplemental computation module 24 in FIG. 2B depends on the particular application of the invention.
  • the EGSV computation module 22 together with the structured quadratic form computation module 42 are part of an iterative computational loop that produces a series of successively improved values for an EGSV of a source signal, starting from an initial value.
  • the EGSV computation module 22 receives as input a cumulant strength value for the current value of an EGSV. Based upon this cumulant strength, the module 22 determines a new value for the EGSV that will cause the value of the cumulant strength value to increase in absolute value. This new EGSV is output to the structured quadratic form computation module, where it replaces the old EGSV, and the computation cycle is repeated until a termination test is satisfied.
  • FIG. 2C depicts the direct computation method of the invention in conceptual form using the same basic structure as FIGS. 2A and 2B.
  • the EGSV computation module in this figure is referred to by numeral 22' , since it performs its function differently from the module 22 in FIGS. 2 A and 2B.
  • the EGSV computation module 22' receives as input the C matrix computed by the cross-cumulant matrix computation module 40.
  • the EGSV computation module 22' computes the solutions to a fourth degree polynomial equation, from which the EGSVs of one or two sources are directly determined. The mathematical details of the particular polynomial equation are described in a Section 7.0.
  • the values of the EGSVs, so determined, are output to the supplemental computation module 24.
  • FIGS. 2A, 2B and 2C illustrate the processing required to recover a single source signal from among a plurality of possible source signals, for purposes of explanation. We will now turn to the case of recovery of multiple signals.
  • the present invention has a number of applications in the communications field and, more specifically, in the separation of cochannel signals.
  • FIG. 3A illustrates the concept of signal separation using the first iterative method (10,
  • FIG. 1 and differs from FIG. 2 A in that there are multiple linear combiners 26.1 through 26. P and multiple estimated generalized steering vector (EGSV) computation modules 22.1 through 22. P.
  • the supplemental computation module 24 has been replaced by a supplemental computation module 24 A for signal recovery, the details of which will be explained with reference to FIG. 4A.
  • the sensor receiving system 20 outputs the received signals in conditioned form on line 28, which is connected to each of the linear combiners 26 and EGSV computation modules 22.
  • Signal conditioning in the sensor receiving system, 20, which is described subsequently, may be used to transform the received signals to P sets of signals, where P is the number of signal sources being received, and to be separated. As in FIG.
  • the EGSV computation modules 22 and the linear combiners 26 cooperate to produce converged values of estimated generalized steering vectors on output lines 28.1 through 28.
  • the supplemental computation module 24A uses these generalized steering vectors and the received signals on lines 28 to generate P separate recovered signals on lines 38. How this latter step is accomplished is best understood from FIG. 4A, which will be discussed after consideration of FIGS. 3B and 3C.
  • FIG. 3B depicts a cochannel signal separation and recovery system similar to that of FIG. 3A, but using the second iterative method (12, FIG. 1).
  • FIG. 2B there is a single cross-cumulant matrix computation module 40 but, unlike the FIG. 2B system, there are multiple (P) structured quadratic form computation modules 42.1, 42.2 ... 42. P, each of which receives cross-cumulants from the matrix computation module 40 and conditioned input signals from line 28.
  • Cumulant strength values generated by the structured quadratic form computation modules 42 are supplied to respective EGSV computation modules 22.1, 22.2 ... 22.P, which output recomputed EGSVs for feedback over lines 34.1, 34.2 ... 34.P, respectively, to the structured quadratic form computation modules 42.1, 42.2 ... 42. P, and converged values of the EGSVs on lines 36.1, 36.2 ... 36.P.
  • FIG. 3C depicts a signal separation and recovery system similar to those of FIGS. 3 A and 3B, but using direct computation instead of an iterative method.
  • This figure is also closely similar to FIG.2C, except that the ESGV computation module 22' generates two EGSVs on output lines 36.1 and 36.2, and supplemental processing is performed in the supplemental computation module for signal recovery, which generates two recovered signals for output on lines 38.1 and 38.2.
  • the supplemental computation recovery module for signal recovery 24A includes a recovery beamformer weight vector computation module 44 and multiple recovery beamformers 46.1 through 46. P. Computation of the weight vectors in module 44 is made in accordance with a selected known technique used in signal processing.
  • the beamforming weight vectors for signal recovery are computed directly from the generalized steering vectors. This is done by one of two methods: (1) by projecting each generalized steering vector into the orthogonal complement of the subspace defined by the span of the vectors of the other sources by matrix transformation using the Moore-Penrose pseudo-inverse matrix; or (2) by using the Capon beamformer, also called the Minimum Variance Distortionless Response (MVDR) beamformer in the acoustics literature.
  • the module 44 generates, from the P input estimated generalized steering vectors (EGSVs), a set of P weight vectors w, through w P , on lines 48.1 through 48. P.
  • Implicit in the function performed by the module 44 is the orthogonalization of the output signals, such that the weight vectors are mutually orthogonal, i.e. each is representative of a separate signal.
  • the module 44 may be implemented in the manner described in Section 3.0.
  • the weight vectors w, through w P are applied to the received, conditioned signals on line 28, in the recovery beamformers 46.1 through 46. P. This is basically a process of linear combination, wherein each signal component is multiplied by a corresponding component of the weight vector and the results are added together to produce one of the recovered signals.
  • the functions of the linear combiners 26 in the case of FIG. 3A), the EGSV computation modules 22, the recovery beamformer weight vector computation module 44 and the recovery beamformers 46 may be combined in various ways. Moreover, all of these functions, together with signal conditioning performed in the sensor receiving system 20, may be conveniently implemented in one or more integrated circuits.
  • Signal separation in accordance with this invention has important advantages in the context of multipath signal processing.
  • Communication signals particularly in an urban environment, often reach a receiver antenna over multiple paths, after reflection from geographical features, buildings and other structures.
  • the multiple signals arrive at the antenna with different signal strengths and subject to relative time delays and other forms of distortion. Because they arrive from different directions, such signals may be separated in a conventional cochannel signal separation system.
  • the cochannel signal processing system of the present invention will automatically combine the multipath components if they are still coherent with each other.
  • Coherency in this context, is a relative term that simply indicates the degree to which the signals are identical.
  • Multipath components are noncoherent, and therefore not combinable, when they suffer large relative time delays or when a signal transmitter or receiver is in motion, causing a Doppler shift in the transmitted signal that affects one path more than another.
  • each generalized steering vector referred to with reference to FIGS. 1, 2A-2C and 3A-3C corresponds to the sum of all of the mutually coherent multipath components of a signal source incident on the sensor receiving system 20.
  • the generalized steering vectors from all signal sources are then converted to a set of beamforming weight vectors (in recovery beamformer weight vector computation module 44), without the need for knowledge of array geometry or array manifold calibration data (which relates array steering vectors with angles of arrival for a particular array).
  • the invention is, therefore, completely “blind" to the array manifold calibration data and the physical parameters of the array.
  • FIGS. 2A-2C, 3A-3C and 4A provides for cochannel signal separation, even in a multipath environment, without use of specific sensor array manifold information.
  • the array parameters, and specifically array calibration data that relate array steering vectors with directions of signal arrival, are needed if one needs to know the angular directions from which signals are being received.
  • FIG. 4B illustrates this concept for a single signal source referred to as source k. Although the concept depicted in FIG. 4B is conventional, the manner in which the present invention generates the input steering vectors is novel.
  • a generalized steering vector, referred to as a* is received from one of the EGSV computation modules 22. k (FIG. 3A) on line 36.
  • the DOA search module 14B uses stored array calibration data 50, which associates each possible direction of arrival with a steering vector. These calibration data are typically stored in a memory device as a lookup table. In the DOA search module 24B, a reverse table lookup is performed to obtain the two closest directions of arrival from the input steering vector, as indicated in block 52. Then a more precise angle of arrival is obtained by performing an interpolation between the two angle values retrieved from the calibration data 50, as indicated in block 54.
  • the direction of arrival (DOA) parameter for the k ⁇ signal source is output from computation module 24B on line 38.k.
  • an important application of the invention is to two-way communication systems.
  • the allocation of transmission frequencies within a geographical area is often a limiting factor that determines the maximum number of active users that the system can handle.
  • information derived from signals received and separated in a receive mode of operation are used to generate signals to a transmit antenna array, in such a way that separate information signals can be transmitted to respective remote stations using the same frequency.
  • the present invention makes use of an array of sensors or antennas to separate received signals of the same frequency (cochannel signals).
  • cochannel signals In a two-way communication system of the type having multiple mobile units, it would be impractical, in general, to require the use of an antenna array at each remote or mobile transmitter/receiver.
  • These remote units may be installed in vehicles or be hand-held devices for which the use of an antenna array is either inconvenient or simply impossible.
  • the communication path between remote units is completed through one or more base stations operating as a receiver and transmitter. Since the base stations are generally larger and more powerful than the mobile units and are fixed in location, they may be conveniently structured to include antenna arrays for both receiving and transmitting.
  • a receive array connected to the system of the invention provides for separation of received cochannel signals.
  • an important by-product of the signal separation process is a set of generalized steering vectors, each associated with a separate signal source.
  • received signals are recovered on line 38, in the manner discussed with reference to FIG. 2A.
  • a transmitter 56 uses the generalized steering vector corresponding to the received signal source, obtained from the EGSV computation module 22, in order to generate a weight vector for application to a transmit antenna array (not shown).
  • the transmitter 56 also receives, on line 58, an information signal to be transmitted. Typically, this will be in the form of a digitized voice signal, although it may be a data signal of some other type.
  • the transmitter 56 generates the transmit weight vector in accordance with a technique to be described below in Section 12.0, modifies the information signal in accordance with the weight vector, modulates a carrier signal with the weighted information signal components, and sends a set of signals to the transmit array, as indicated by lines 60.
  • the carrier frequency although the same for each of the transmitted cochannel signals, is usually selected to have a frequency different from the receive signal frequency.
  • the transmitter 56 generates multiple weight vectors, which are applied to the respective information signals to be transmitted, then linearly combined, antenna element by antenna element, and finally modulated to produce a set of composite antenna element signals for coupling to the transmit antenna array.
  • this aspect of the invention provides a simple but effective technique for use in a communication system base station, for receiving cochannel signals from, and transmitting cochannel signals to, multiple remote stations in close proximity to a base station and to each other. Limited only by the number of elements in the receive and transmit arrays, the technique allows for re-use of the same frequencies in the multiple remote stations, with a resultant increase in system capacity or user density, e.g. the number of users per frequency per unit area (per cell or sector thereof).
  • the "incident signals" 27 shown in FIGS. 2A-2C and 3A-3C are signals transmitted through space, the atmosphere, the ocean or some other relatively unbounded medium.
  • cochannel signals transmitted on a waveguide of some type may also be received and processed in accordance with the principles of the invention.
  • waveguide is used in quotation marks in the heading of this descriptive subsection because the term is not intended to be limited to a waveguide operating at microwave frequencies, or to an optical waveguide in the form of an optical fiber or planar optical waveguide.
  • the word waveguide as used in this specification is intended to cover any of various types of bounded transmission media, including microwave waveguides, optical waveguides, coaxial cables, or even twisted pairs of conductors operating at relatively low frequencies.
  • a common attribute of these "waveguides" is that multiple signals may be transmitted along them using the same frequency but different modes of transmission, such as different polarization modes for microwave or optical waveguides, or, in the case of twisted-pair conductors, different signals being applied to different wire-to-ground combinations.
  • the propagation modes may become scrambled in the propagation medium and may become difficult or impossible to separate in a receiver.
  • FIG. 6 a system in accordance with the invention can be usefully employed to separate such signals. Except for the sensor receiving system 20, the system of FIG. 6 is identical with the FIG. 3 A system for separating received signals.
  • the “incident signals” 27 are received from a signal “waveguide” 62, as defined in the preceding paragraph, and are sensed by “waveguide” sensors 52.
  • the sensors 64 take the form of electromagnetic probes or optical sensors appropriately inserted into the waveguides.
  • the sensors may include appropriate circuitry connected to the conductors and ground.
  • Signals from the sensors 64 are subject to received signal conditioning, as indicated in block 66, and are then input to the signal separation system of the invention in the same way as signals from multiple elements of an antenna array. The system recovers the original signals as indicated on lines 38.
  • Another application of the invention is similar in some respects to the recovery of signals from a bounded waveguide.
  • the "waveguide" sensors 64 are adjacent playback sensors in a magnetic recording apparatus. There is no "waveguide” as such; nor are the signals transmitted through an unbounded medium. Instead they are sensed electromagnetically from a recording medium on a moving magnetic tape or disk.
  • FIG. 7 provides the basis for this overview. Some of the technical terms used in FIG. 7 are introduced for the first time in this specification and may not be completely clear until the complete description is studied.
  • the signal separation and recovery process involves a number of manipulations of the estimated generalized steering vectors (EGSVs) pertaining to the multiple signal sources.
  • EGSV initialization is performed. This is simply the selection of initial EGSV values (on line 30 in FIGS. 2A and 2B) from which to begin processing. It will be recalled that processing is performed in a batch mode in which sequential blocks of data are processed.
  • the initial EGSVs may be estimates carried forward from an already processed previous block of data, or they may be generated from scratch, using either random quantities or a cumulant eigendecomposition algorithm.
  • Another function performed in block 70 is to "project" the initial EGSVs into a P-dimensional signal space, where P is the number of signal sources.
  • the antenna array provides sets of input data signals that are M-dimensional, where M is the number of elements in the array. Throughout the computations performed in accordance with the invention, there is often a design choice to be made because the mathematical manipulations may be performed on these M-dimensional vector quantities, or on corresponding P-dimensional quantities, where P is the number of signal sources.
  • the recovered signals are output as P one-dimensional signals, since there is one recovered signal per source, but signal recovery requires beamforming in the sensor space.
  • Block 72 in FIG. 7 describes EGSV prioritization. This aspect of the invention has not yet been discussed but, simply stated, prioritization is needed to provide a rational basis for choosing which of multiple signals should be recovered first.
  • the sensor array having M elements, inherently limits the number of cochannel signals that may be recovered to M. If more than M signal sources are actively transmitting, the first M signals are selected on the basis of their non-Gaussianity, as determined by either of two methods: using the C-matrix or beamforming and computing cross-cumulants.
  • the resulting priority list of sources is passed to a signal separation iteration block 74, which uses one of the two iterative methods to obtain convergence for each EGSV in turn, starting with the highest priority source.
  • the steps of the iterative procedure include updating the EGSV using either the C-matrix or the beamforming and cross-cumulant computations, then using a conventional technique, such as the Gram-Schmidt procedure, to ensure that each EGSV is orthogonal to already -processed higher-priority EGSVs. These steps are repeated until convergence is achieved for each signal source.
  • Block 76 of FIG. 7 describes another practical issue in signal recovery systems that use batch processing.
  • Each "port" from which a recovered signal is output in processing a block of data must be correctly associated with a recovered signal from the previous data block. This association is performed by comparing the EGSVs. The ports cannot be associated merely on their positions in the priority list because signal sources may come and go from list as time passes.
  • phase angle of an EGSV generated in processing one block of data may not exactly match the phase angle as determined in the next block.
  • This processing step applies an EGSV phase adjustment to eliminate any discontinuity from block to block.
  • block 80 of FIG. 6 the EGSVs are "backprojected" from the
  • the present invention pertains to a system and method for processing and recovering cochannel signals received at a sensor array.
  • CURE cumulant recovery
  • this embodiment uses eigenvectors and eigenvalues in part of its computation, it is referred to as the eigenCURE system, or simply the eCURE system.
  • the embodiment disclosed uses the first iterative method (the beamform and cross-cumulant method, first introduced at 10 in FIG. 1). It will be understood, however, that the eCURE system may be modified to use the C-matrix iterative method (12, FIG. 1).
  • FIG. 8A shows the basic components of the eCURE system, including a sensor array, indicated by reference numeral 110, which receives signals from various directions, as indicated at 112, a bank of receivers 114, and a sampler and analog-to- digital converter bank 116.
  • the separate signals from the elements of the sensor array 110 are coupled directly to the receiver bank 114, which performs conventional filtering and frequency downconversion functions.
  • the sensor signals are then sampled at a high rate and converted to digital form in the sampler and analog-to- digital converter bank 16. At this point, the signals have been filtered, downconverted and digitized and processing is about to begin.
  • each sensor in the array 110 provides a stream of digitized signals, processing may be conveniently performed in batches or blocks of data. From time to time, reference will be made in this description to current and previous data blocks.
  • the block size is critical only in the sense that the size selected affects processing speed and accuracy of estimation.
  • the first major processing step is preprocessing the sensor data, which is performed in a preprocessing computer 120.
  • the preprocessing computer 120 performs four important functions:
  • the other major components of the cochannel signal recovery system are a signal recovery controller 122, multiple signal extraction ports 124.1 through 124. L, an orthogonalizer 126, and multiple demodulators 128. Preprocessed sensor signals are transmitted over lines 130 in parallel to each signal extraction ports 124.1. Each port in this embodiment of the invention is implemented as a separate computer processor. Using the iterative technique described above with reference to FIGS. 2 A and 3 A, each of the signal extraction ports 124 generates output signals derived from a separate source. These signals are output on lines 132 to the demodulators 128, which produce usable output signals on lines 134. If the information contained in the signals is audio information, the outputs on lines 134 may be connected to separate loudspeakers or other audio processing equipment (not shown).
  • the function of the orthogonalizer 126 is to ensure that each of the ports 124 is associate with a separate signal source.
  • the signal recovery controller 122 performs various control functions in conjunction with the signal extraction ports 124 and the orthogonalizer 126.
  • the controller 122 receives the source count estimate P e from the preprocessing computer 120 over line 136 and also receives eigenstructure parameters from the preprocessing computer over line 138. The latter are also transmitted to the signal extraction ports 124, and the source count estimate is transmitted to the orthogonalizer 126.
  • the controller 122 also sends a priority list to the orthogonalizer 126 over line 140.
  • the controller 122 sends adaptation flags to the signal extraction ports 124 over lines 142 and receives capture strength values from the signal extraction ports over lines 144.
  • the controller 122 is to keep track of signal sources as they appear and disappear and to make sure that the signal extraction ports 24 and the orthogonalizer 126 handle appearing and disappearing signal sources in an appropriate manner.
  • the sensor receiving system 20 includes the sensor array 110, the receiving bank 114, the sampler and analog-to-digital converter 116 and the preprocessing computer 120.
  • the functions performed by the linear combiners 26, the EGSV computation modules 22 and the supplemental computation module for signal recovery 24A, are performed in the signal recovery controller 122, the signal extraction ports 124 and the orthogonalizer 126.
  • FIG. 8B is a hardware block diagram of one implementation of the eCURE system of the present invention, as used to separate multiple signals containing audio information. Identical reference numbers have been used in this figure to refer to components that appear in both FIG. 8 A and FIG. 8B.
  • the preprocessor computer 120 is implemented as a separate circuit card. It is of little significance whether the preprocessor computer is implemented on a single circuit card or in a stand-alone computer. The functions performed in both cases would be identical.
  • the controller 122 and orthogonalizer 126 are implemented in one computer on a single circuit card, as indicated in FIG. 8B.
  • the signal extraction ports 124 one of which is shown, are implemented on separate computers on circuit cards and the demodulators 128 are also implemented on a separate computer.
  • All of the computers mentioned above may be of any appropriate type, but in a presently preferred demonstration embodiment, they are Intel model i860 processors.
  • the computers are connected to a high-speed crossbar switching network 150, such as Part No. ILK-4, from Mercury Computer Systems, Lowell, MA, 01854.
  • the sensors 110 may be of any appropriate type, such as Part No. 10- 183-244, from TRW Inc., Sunnyvale, CA, 94088.
  • the receivers 114 in this embodiment include a VME synthesizer module (Part No. 1600M SYN-5, from APCOM Inc., Gaithersburg, MD, 20878), a local oscillator module (Part No. 1600M LOD-1, also from APCOM Inc.) and a VME RF converter module (Part No. 1600M RFC -5, also from APCOM Inc.).
  • the digitizer bank 116 may include an Access256 motherboard (Part No. MOB256-4) and analog input and digital output cards from Celerity Systems, Inc., San Jose, CA, 95117.
  • the receiver bank 114 tunes each sensor to the desired frequency and downconverts any signals received.
  • One receiver is allocated to each sensor.
  • the receivers 114 enable the system to isolate a single frequency of interest and to translate it to a frequency where it can be more conveniently digitized and processed. No demodulation is performed at this point.
  • one embodiment of the invention has receivers that downconvert the received signals to a center frequency of 225 kHz and a bandwidth of 100 kHz (specifically the 3 dB bandwidth).
  • the digitizer bank 116 converts the received signals to digital samples. Only real samples, not complex samples, are generated at this stage.
  • the digitizer bank 116 consists of a digitizer motherboard and an input daughtercard that samples up to eight channels simultaneously at a rate of up to 10 megasamples per second.
  • the samples are exported for further processing through an input card 151.
  • the data processing system performs digital filtering of the input data, with the digital filtering card 152, and converts the real samples to complex values needed for processing, using a real-to-complex conversion card 153.
  • a system manager 154 in the form of a Model 68040 CPU (Part No. CPU-40B/ 16-02, from Force Computers Inc., San Jose, CA, 95124) controls this demonstration system, with operator interface being provided by a workstation 56, such as a Powerlite notebook workstation (Part No. 1024-520-32, from RDI Computer Corporation, San Diego, CA, 92008).
  • a workstation 56 such as a Powerlite notebook workstation (Part No. 1024-520-32, from RDI Computer Corporation, San Diego, CA, 92008).
  • the demodulated signal outputs are connected to loudspeakers 157 and a hard disk drive 158 is provided for storage of received messages.
  • the system manager tunes the receivers 116, instructs the digitizer to record samples, configures and initiates operation of the data processing system, and controls a peripheral interface board (Part No. MVME 162-63, from Motorola Computer Group, Tempe, AZ, 85282), through which communication is had with the loudspeakers and the hard disk.
  • a peripheral interface board Part No. MVME 162-63, from Motorola Computer Group, Tempe, AZ, 85282
  • FIG. 9 which is broken into two figures designated FIGS. 9 A and 9B, is a block diagram similar to FIG. 8 A but showing in each block more detail of the functions performed by each component of the system. The functions will be described with reference to the more detailed diagrams that follow.
  • Another feature shown in FIG. 9 but not in FIG. 8A is the output of generalized steering vectors on lines 170.
  • the generalized steering vectors as discussed earlier in this specification, are an important product of the signal recovery process, along with the recovered signal outputs.
  • the generalized steering vectors are shown as being used in a DF search processor 172, which generates signal source bearings or directions of arrival (DOA), output on lines 174, consistent with the description with reference to FIG. 4B.
  • DOA directions of arrival
  • the preprocessor performs a block-by -block analysis of "snapshots" of data from the sensor array, which has M elements.
  • the preprocessor determines the eigenvectors, the number of signal sources P, and the signal subspace of the received array measurement data.
  • the preprocessor also filters the received array data, transforming it from M-dimensional sensor space to P-dimensional signal subspace. This transformation also renders the steering vectors of the transformed sources orthogonal to each other, which greatly accelerates convergence on recovered signal solutions later in the processing, and the transformed source powers are made equal.
  • the sampled sensor signals represented by a function of time r(t), are input to the preprocessing computer until a complete block of data has been received. While the block is being further processed, another block of data is input to a buffer in the computer (not shown). As indicated at 180, each block of data is first subject to computation of an array covariance matrix for the current block of data.
  • the sample covariance matrix R from N samples or "snapshots" of data is given by:
  • R ⁇ r(t)r H (t) .
  • eigendecomposition which decomposes the covariance matrix as:
  • the preprocessing computer uses a combination of two estimates of the number of sources, as indicated in block 184: the estimate determined by Akaike's Information Criteria (AIC) and the estimate determined by Rissanen's Minimum Description Length principle (MDL). Specifically, the preprocessing computer averages the AIC and MDL functions and finds the single maximizer of the average.
  • AIC Akaike's Information Criteria
  • MDL Rissanen's Minimum Description Length principle
  • the preprocessor computer computes estimates of the eigenvectors and eigenvalues of the signal and noise subspaces:
  • E s and E Albany are estimates of signal and noise subspace eigenvectors, respectively, and ⁇ I is an estimate of the noise power.
  • the diagonal matrix A s contains the estimates of signal subspace eigenvalues.
  • the transformation reduces the dimensionality of the sensor measurements from M, the number of sensors, to P e , the estimated number of signal sources.
  • the eigendecomposition performed in the preprocessing computer is a well known technique, sometimes referred to as spatial prewhitening, originally developed for use in passive sonar signal processing. It is described in more detail in a number of texts on signal processing. For example, see “Detection of Signals in Noise,” by Anthony D. Whalen, Academic Press, New York (1971), beginning at page 392.
  • spatial prewhitening noise components at each sensor are assumed to correlated (i.e., not completely random noise).
  • the prewhitening process operates on the noise signals to render them uncorrelated (i.e,. "whitened”).
  • eigendecomposition is to use covariance inversion in preprocessing.
  • the latter process whitens both noise and signal components of the sensor signals and it can be at least intuitively understood that this is a less desirable approach since it renders the signals less easy to separate from the noise than if spatial prewhitening of the signal only were used.
  • the present invention will still separate cochannel signals using covariance inversion instead of eigendecomposition as a preprocessing step.
  • eigendecomposition, or spatial prewhitening is the preferred approach for preprocessing.
  • FIG. 11 A single active signal extraction port, one of the L ports 124 shown in FIG. 8 A, is shown in detail in FIG. 11, which extends over two sheets, as FIGS. 11A and 11B.
  • L is the number of physical ports allocated for signal extraction and is less than M, the number of sensors.
  • the illustrated port is referred to as the kth port 124. k, and signals pertaining specifically to this port are referred to by signal names that include the prefix k.
  • An "active" port is one that has been assigned responsibility for separating a signal from the received signal data. The functions performed in an active port are critical to the invention and are illustrated in FIG. 11.
  • the active signal extraction port 124.k receives as inputs over line 130 the preprocessed sensor data y(t).
  • the sensor signals are described as being processed in blocks, although it will be understood that an alternative embodiment of the invention could be devised to process the signals continuously.
  • the principal output from the port is a recovered signal, designated g k (t).
  • An important intermediate output is a vector quantity called the normalized cumulant vector, referred to as b .
  • Another important output is the generalized steering vector a k , which defines the directional location of the signal source.
  • the quantities related to the mth analysis block are identified in this descriptive section by using the additional index m. For example, the steering vector estimate provided by the kth port after the mth block is processed is denoted as a k (m).
  • the time variable t spans from the start of the mth block to the end of the mth block, and, with N snapshots per block, can be expressed at t e [(w - ⁇ )N + l,/wN].
  • the quantities E, ⁇ and T are obviously obtained by processing the mth block of data, so, for simplicity, they are not written with the (m) index.
  • the steering vector a*(0) is set to an initial random vector with a unit norm. As shown in processing block
  • the steering vector a t (m-l) is first projected to the signal subspace by transforming it into a value ⁇ k , using the transformation T, which is input to block 202 as shown.
  • the steering vector projected into the signal subspace is computed as:
  • ⁇ k T a k (m - l).
  • auxiliary source waveform is provided to the cumulant vector computer 206, which computes sample estimates of the cross-cumulant vector b k , which has components defined by:
  • (b k ) i c (u k (t),u k (t),u k (t), yi (t)) , l ⁇ i ⁇ P e , where (b t ), denotes the ith component of b k .
  • the asterisk indicates that the conjugate of the process u k (t) is used in two of the four quantities of which the cumulant is computed.
  • the cumulant vector computer computes a fourth-order cumulant vector of the input signals. More specifically, the vector is the cumulant of four quantities derived from the input signals.
  • the capture strength c k is computed in a capture strength computer 208.
  • the capture strength computer 208 determines the convergence condition of a port by evaluating the degree of non-Gaussianity of the auxiliary signal u k (t), and the amount of change between b k and ⁇ k . At convergence, these two vectors should be pointing at the same direction.
  • the non-Gaussianity of the auxiliary signal can be determined from the ratio of its fourth-order cumulant to its squared power: cum(u k (t),u k (t),u (t),u k (t)) ⁇ ⁇ t ' (E ⁇ M ,(tK * (t) ⁇ ) 2
  • the denominator can be computed from the auxiliary signal u k (t). Since the covariance matrix of the signal component of y(t) is the identity matrix, the denominator can be ignored when the signal to noise ratios (SNRs) are high enough.
  • SNRs signal to noise ratios
  • the capture strength c k can be determined from ⁇ k and ⁇ k .
  • c k ⁇ k .r ⁇ k .
  • the capture strengths c k are provided to the controller unit for priority determination.
  • the cross-cumulant vector is normalized by its norm and fed to the orthogonalizer:
  • the orthogonalization unit uses the Gram-Schmidt procedure and outputs orthogonalized cumulant vectors (d vectors) to the ports.
  • the steering vectors are provided to the controller unit in order to determine the port that loses its signal in the event of a source drop-out or to determine which port will be activated in the case of a new signal.
  • the steering vectors can also be used by an optional DF search unit to determine the source bearings.
  • the active port determines the signal waveform from the source it is tracking. However, it is necessary for an active port to maintain gain and phase continuity of its recovered signal at block transitions in order to prevent block-to-block gain and phase modulation of the recovered signal. To accomplish this goal, we need to examine the properties of the algorithm in more detail.
  • the kth block scales the steering vector estimate by q k , relative to the previous block. Therefore, it scales the waveform estimate at the mth block by the reciprocal of this quantity.
  • the second way to compute q k is to force the first component of the steering vector estimate to be unity. In this process, we simply let q k be the first component of a k (m).
  • w k will be determined using the orthogonalized cumulant vector d k (m) and q k .
  • the signal recovery controller 122 (FIG. 8A) operates because this affects operation of the orthogonalizer 126 and the signal extraction ports 124.
  • an important function of the controller 122 is to detect changes in the status (ON or OFF) of signal sources and to identify lost sources.
  • the controller 122 maintains a priority list of ports and a related set of adaptation flags that indicate which ports are active.
  • the functions of the controller 122 include logic to detect changes in the number of signal sources, indicated by block 220, port allocation logic 222, priority list determination logic 224, and adaptation flag logic 226.
  • the logic 220 to detect changes in the number of sources assumes that there is no more than one change in the number of sources from one data block to the next.
  • the function of the port allocation logic 222 is to determine which of the active ports 124 was last processing signals from the source that has just been lost.
  • the basic principle employed to make this determination is to identify which port has a steering vector with the greatest component in the current noise subspace.
  • Each signal that contributes to the measurements has a steering vector that is orthogonal to the current noise subspace determined from the sample covariance matrix R .
  • the signal subspace for the two active signals includes the x-axis and y-axis directions.
  • the noise subspace is the space defined by all remaining axes in the space. In this case, the noise subspace eigenvector is in the z-axis direction.
  • the current noise subspace When a signal disappears and drops out of consideration, the current noise subspace then includes the space previously occupied by the signal. In the three- dimensional example, if the x-axis signal disappears, leaving only the y-axis signal, the noise subspace is redefined to include a plane in the x and z directions.
  • the port allocation unit uses the steering vector estimates from the previous data block (indicative of the active sources before one was lost), and projects these vectors into the noise subspace as defined for the current data block.
  • the steering vector from the previous data block that lies completely in the current data block noise subspace, or the one that has the largest component in the noise subspace, is determined to be the signal that was lost between the previous and current data blocks.
  • the logic 222 obtains the steering vector estimates from all of the ports that were active in the previous data block, and normalizes them (i.e. , scales them have a unit norm). Steering vector estimates are obtained for only the first (P e + l) ports in order of decreasing capture strength.
  • the port allocation logic is concerned with the direction of the steering vectors in space and any differences in magnitude arising from different signal strengths should be eliminated. Then the normalized steering vectors are projected onto the current noise subspace, as provided from the preprocessing computer 120 in the form of noise subspace eigenvectors E n .
  • the port allocation logic 222 computes the "leakage" of the steering vector of the kth port into the current noise subspace from:
  • Port 1 was locked onto Source 3
  • Port 3 was locked onto Source 1
  • Port 5 was locked onto Source 2.
  • Source 3 turned off just before the current block and that the following capture strengths were determined for the previous and current data blocks:
  • Port 2 and Port 4 were inactive and had their capture strengths set to 0.001.
  • Source 3 turned off, the port allocation logic 222 determined that Port 1 had lost its signal, using the analysis discussed above.
  • Inherent in the list of capture strengths is a priority list of ports (i.e., a list of port numbers in order of decreasing capture strength). Therefore, the priority list based on the previous data block is [3,1,5,2,4] and the priority list based on the current data block is [3,5,1,2,4].
  • the convention adopted is that, when ports have identical capture strengths, they are assigned priorities based on port numbers.
  • the priority list determination logic 224 generates the priority list in this manner, based on the capture strength list transferred from the port allocation logic 222.
  • the priority list is used by the adaptation flags logic 226 to generate a list or vector of adaptation flags.
  • the adaptation vector contains elements, where L is the number of physical ports in the system.
  • the adaptation flags vector for the previous data block is [1,0,1,0,1] and for the current data block is [0,0,1,0,1].
  • the adaptation flags vector is supplied to multiple port signal recovery unit (124, 126, FIG. 9), and specifically to the signal extraction ports 124.
  • the priority list is also supplied to the multiple port signal recovery unit, and specifically to the orthogonalizer 126, which will be discussed in the next descriptive section.
  • the purpose of the priority list is to facilitate an orderly allocation of signal sources to ports, from the lowest port number to the highest. Further, when a signal source turns on, it is desirable that the most recently freed port be made available for assignment to the new signal, to provide continuity when a source turns off and on again without a change in the status of other sources.
  • the Port Allocation Unit computes the port's leakage from (here
  • the capture strength of a port is computed by the multiple port signal recovery unit described earlier in this specification. The controller, however, can overwrite the computed value as described above.
  • the orthogonalizer 126 functions to ensure that each port is consistently assigned to process only one signal source, which is to say that each active port captures a different source.
  • the orthogonalizer 126 receives a normalized cumulant vector from each active port, the vector being represented by b k for the kth port.
  • the orthogonalizer 126 outputs back to each port an orthogonalized cumulant vector, which is d k for the kth port.
  • the orthogonalizer also receives the priority list from the signal recovery controller 122, so has knowledge of the identities of the active ports and their respective associated capture strengths, and also receives the estimated number of signals P e , from the preprocessing computer 120.
  • the orthogonalizer forces the active ports to capture different sources by orthogonalizing their cumulant vectors, which, in turn, are estimates of the steering vectors in the dimensionally reduced space.
  • the cumulant vectors for active ports should be orthogonal to each other, to cause the ports to capture different source signals and to prevent two ports from locking up on the same source signal.
  • the orthogonalizer From the priority list and the estimated number of signals P e , the orthogonalizer forms a P e by P e matrix Z from the active port steering vectors b ⁇ such that the kth column of the matrix Z is the steering vector of the port that is the kth item in the priority list.
  • the orthogonalizer uses a known procedure known as the classical Gram-Schmidt (CGS) algorithm to perform the orthogonalization operation.
  • CGS Gram-Schmidt
  • the Gram-Schmidt algorithm is described in a number of texts on matrix computations, such as Matrix Computations, by Gene H. Golub and Charles F. Van Loan (The Johns Hopkins University Press, 1983), pp. 150-154.
  • the Gram-Schmidt algorithm is applied to the matrix Z to obtain a decomposition of the type:
  • a port When a port is determined to be inactive, as indicated by a zero adaptation flag, the port performs three simple functions, as shown in FIG. 14. First, its output signal gj ⁇ f) is set to zero. Second, its capture strength c k is set to a minimum value MIN. Finally, the last steering vector a k , estimated in the port just before it became inactive, is stored in a memory device associated with the port, to facilitate recapture of the same signal that was lost, if it should turn on again in the near future. More specifically, the adaptation flag input to the inactive port is delayed by one data block time.
  • the port stores a steering vector either from two blocks earlier, if the delayed flag has a value of 1, or one block earlier, if the delayed flag has a value of zero.
  • CiCURE Covariance Inversion Cure
  • CiCURE The basic cumulant recovery (CURE) system described in Section 3.0 uses eigendecomposition in preprocessing and is referred to as eigenCURE (eCURE) for convenience.
  • eCURE eigenCURE
  • Another variant of the CURE system uses covariance inversion instead of eigendecomposition and is referred to as covariance-inversion CURE (CiCURE).
  • CiCURE is best thought of as a low-cost approximation to the high- performance eCURE system. As such, it shares most of the same advantages over standard CURE as the eCURE method. Certain conditions must be met in order for CiCURE to mimic eCURE and realize the same advantages. The conditions are:
  • CiCURE Under stationary or steady-state signal conditions, this implies a need for a sufficiently long processing block size. Under these conditions, the prewhitening transformation used in CiCURE is a good approximation to that used in eCURE, and the two systems have similar performance properties.
  • This section of the specification describes the components of a signal separation/recovery system that is based on the CiCURE algorithm.
  • the CiCURE signal separation/recovery system incorporates a spatial prewhitening transformation based on the inverse of the input sample covariance matrix (this operation is performed by using an eigendecomposition in the eCURE).
  • the received signal data is filtered or transformed by the prewhitening operation in the CiCURE, unlike eCURE.
  • the prewhitening done in the CiCURE is implicit in the mathematics of the signal recovery ports, and it is only necessary to compute a matrix decomposition of the input sample covariance matrix. This latter operation is done in a preprocessor, whose output is made available to all of the signal recovery ports. Key characteristics of the implementation described below are that the iterative convergence of the CURE algorithm is realized over several blocks instead of within a single block, and "high-priority" ports converge sooner than "low-priority” ports.
  • a signal recovery system based on the CiCURE method is simpler than one based on the eCURE.
  • demodulators to complete the recovery of the separated signal for the purpose of recording or listening
  • DF direction-finding
  • CiCURE method is shown in FIG. 15. The details of the preprocessor 120' and the signal recovery ports (124.1, 124.2, etc.) that are unique to the CiCURE method are described below. All other system details are as previously described for the eCURE system.
  • the preprocessor 20' computes a matrix decomposition of the input sample covariance matrix. It does this on a block-by-block basis by first computing the sample covariance matrix of the array snapshots within a processing block and then computing the Cholesky decomposition of the sample covariance matrix.
  • the Cholesky decomposition is output to the signal recovery ports 124.1, 124.2, 124.3, which use this information to adapt their weight vectors to separate the cochannel source signals.
  • the preprocessor 20' first forms the sample covariance matrix from the snapshots according to:
  • R LL" where L is a lower triangular matrix with positive diagonal terms.
  • L is sent to the signal recovery ports 124.1, 124.2, 124.3, as indicated in the drawing.
  • CiCURE there is no controller unit to detect source ON/OFF transitions as in the eCURE system.
  • the signal recovery ports have a predetermined hierarchy or priority order. The first port has highest priority and so on. Therefore, CiCURE is not able to compensate for dynamic changes in the signal environment as can the eCURE algorithm.
  • Each port receives as input the current block steering vector estimates from the ports that are higher in priority, the sensor signal data, and the Cholesky decomposition of the sample covariance matrix for the current block.
  • Each port outputs the recovered signal and associated steering vector for a captured source.
  • FIG. 16 shows the operations of a single signal recovery port.
  • the higher-priority ports produce a set of steering vectors for the current block, defined as ⁇ a ⁇ m), ... , a ⁇ m) ⁇ .
  • the weight vector to produce p k (t) for the current block (the mth block) is determined by modifying the MVDR weights for the kth port on the previous block by a computation carried out in the kth port as described next.
  • the first step is to compute Gram-Schmidt orthogonalized weights (v k (m)) according to:
  • v k (m) w k (m - l) - w k ) ⁇ l (m) / ⁇ a,(m) ⁇ 2 .
  • the port uses the Gram-Schmidt orthogonalized weights (v ⁇ m)) to determine the waveform p k (t) according to Next, a vector, a k (m), of sample cross-cumulants involving this waveform is computed having components:
  • [a t (w)], cum( ⁇ ( ,A (0, ⁇ ( ,r / ( / )), l ⁇ l ⁇ M, in which, [a ⁇ (m)], is the ⁇ h component of a k (m).
  • This vector provides an estimate of a source steering vector and is sent to all the ports that have lower priority than the kth port and the optional direction-finding unit.
  • the MVDR weight vector for the kth port is determined using a k and L.
  • the MVDR weight vector is computed in a two-step procedure that exploits the lower triangular structure of the Cholesky decomposition.
  • the temporary solution, u k is computed by solving the linear system of equations:
  • ⁇ k (m) c k (m)yv k (m)
  • This operation forces the current and previous block signal extraction weights to have a real inner product (i.e., no abrupt phase rotation at the block boundary).
  • This method eliminates block-to-block phase discontinuities, leaving only a bulk phase rotation ambiguity that is constant over all blocks recovered by the port. This bulk phase rotation is unimportant to the recovery of analog AM and FM modulated signals; however, for digital modulations, its removal is desired. Section A subsequent section on phase rotation equalization presents a method for doing so. For now, we skip over this minor detail.
  • the recovered waveform is available for subsequent processing, which may consist of recording of the predetected waveform, demodulation, signal recognition, or other operations.
  • the current block weights are fed into a one-block delay unit which makes them available to the Gram-Schmidt orthogonalization unit as the initial weights for processing the next block.
  • Key characteristics of this implementation are that the iterative convergence of the CURE algorithm is realized over several blocks instead of within a single block, and high-priority ports converge sooner than low-priority ports.
  • eigendecomposition- based CURE eCURE
  • pipeCURE pipelined eigenCURE
  • eCURE eigenCURE
  • the eigenCURE (eCURE) algorithm analyzes measurements on a block by block basis and has dynamic capabilities to eliminate port switching and port allocation in the case of transient sources. Received signal data are filtered or transformed by a prewhitening operation before reaching cumulant based signal separation processing.
  • eCURE (described in Section 3.0) has several advantages over the covariance-inversion CURE (CiCURE) (described in Section 4.0), which uses covariance-inversion instead of eigendecomposition: • It has better signal separation performance (i.e., better crosstalk rejection at port outputs).
  • This last property is particularly useful when trying to recover a weak signal in the presence of strong interfering signals.
  • the pipeCURE system "pipelines" the eCURE algorithm in order to have:
  • the pipeCURE signal separator has three main components, which are shown in FIG. 17: a preprocessor unit 120, which is basically the same as in the eCURE system, a cumulant matrix computer 240, and a multiple port signal recovery unit 242.
  • a preprocessor unit 120 which is basically the same as in the eCURE system
  • a cumulant matrix computer 240 and a multiple port signal recovery unit 242.
  • the direction- finding (DF) search unit 172 to provide copy-aided directions of arrival on output line 174.
  • the preprocessor 120 performs a block-by-block analysis of the element array snapshots, determining the eigenvectors, number of signal sources, and signal subspace of the received array measurement data. It filters the received array data, transforming it from the M-dimensional sensor space to the P-dimensional signal subspace. In so doing, the steering vectors of the transformed sources are made orthogonal to each other in the range of this projection, and the transformed source powers are made equal (at high signal-to-noise ratios). The details of the preprocessor operation were described in Section 3.0 in relation to the eCURE system 5.3 Cumulant Matrix Computer:
  • cumulant matrix computer computes a P ⁇ x p2 (here we assume the number of sources and its estimate are identical) cumulant matrix C is defined as:
  • the multiple port signal recovery unit 242 receives as inputs the preprocessed array measurement y(t), the cumulant matrix C the eigenstructure (E, ⁇ ) derived from the array measurements in the preprocessor 120 and the estimated number of sources (P e ) generated in the preprocessor. Using these input signals, the multiple port signal recovery unit derives recovered signals for output on lines 132 and steering vectors for output on lines 170, in accordance with the following equations and steps: (a) Inputs to multiple port signal recovery unit:
  • Transformation of Steering Vectors Project the steering matrix estimate onto the reduced dimensional space by the transformation matrix T:
  • Priority Determination Reorder the columns of V and form the matrix , such that the first column of W yields the highest cumulant strength, and the last column of W yields the smallest cumulant strength.
  • A E,( ⁇ 1 - ⁇ 2 I, # ) +, ⁇ V.
  • A will be used in the next block as A as an estimate of the steering matrix in the first step of the multiple port signal extraction unit.
  • Beamforming It is important to note that beamforming for P sources requires a matrix multiplication of two matrices: the V matrix that is P by P, and the reduced dimensional observation matrix y(t), which is P by N, where N is the number of snapshots. Usually N is larger than P and this matrix multiplication may take a long time because of its size. Therefore, it may be appropriate to do final beamforming in another processor since it does not introduce any feedback. Final beamforming is accomplished as:
  • update equations can be used principally to provide an improved initial weight vector for each block of array samples (snapshots).
  • These update equations can be used with any of the
  • ⁇ - ⁇ CURE and ⁇ CURE are not independent stand-alone algorithms, but rather are enhancements to CiCURE, eCURE, and pipeCURE that provide greater stability and less port-switching in dynamic situations.
  • k is a time index
  • w t is the linear combiner weight vector that converges to the generalized steering vector of one of the input source signals.
  • the iteration on the index k is on individual snapshots or block of snapshots occurring through time, as opposed to multiple iterations within a block.
  • the ⁇ - ⁇ and ⁇ update method does not preclude iteration within a block. Indeed, within- block iteration can be used in conjunction with block-to-block updating or initialization. Generally, there is no advantage to using the ⁇ - ⁇ or ⁇ update equations for within-block iteration. Within-block iteration should be done by the standard update equation.
  • the ⁇ - ⁇ and ⁇ update equations are best used for block-to-block updating, that is, to initialize a block's weight vector based on the final converged weight vector from the previous block.
  • ⁇ CURE update equation Two equivalent forms are given for the ⁇ CURE update equation. As is discussed below, the first form is most convenient when the purpose is to predict ahead, whereas the second form is most convenient when the purpose is to average previous data with new data.
  • ⁇ - ⁇ CURE and ⁇ CURE appear to involve different update equations, the algorithms are equivalent provided there is a renormalization of the weight vector at every iteration. Because a weight vector renormalization is always used in the iterative steps to prevent the weight vector from shrinking monotonically, a- ⁇ CURE and ⁇ CURE are equivalent.
  • ⁇ - ⁇ CURE and ⁇ CURE updating can be used for determining the initial EGSV(s) of a block of samples subject to eCURE or pipeCURE processing.
  • the block initialization can be performed in either the M- dimensional sensor space or the P-dimensional signal subspace.
  • FIG 19 and FIG 20 show these two cases, respectivly.
  • the application of ⁇ - ⁇ CURE and ⁇ CURE to CiCURE is similar to FIG 19 but is not shown.
  • FIG. 19 shows the relations among various vectors in one cycle of the ⁇ CURE update operating in the M dimensions of the sensor space.
  • Five vectors are shown.
  • w k is the current weight vector at time k and is also the current estimate of the generalized steering vector a k .
  • the generalized steering vector at time k+ l is denoted a /t+ ⁇ - a * + ⁇ is the generalized steering vector of which w k+i is an approximation.
  • the cumulant vector cum vect[cum(.,.,.,.)] is represented as a vector emanating from the origin.
  • the scaled difference vector ⁇ [cum - wj is shown as a vector originating at the tip of w k and extending through the tip of cum. The tip of this vector defines
  • ⁇ is greater than unity, and the algorithm is anticipatory.
  • This form of the ⁇ CURE update is useful when the purpose is to predict a generalized steering vector that is varying with time. It is instructive to think of the ⁇ CURE update as an equation of motion:
  • New Position Old Position + Velocity x Elapsed Time, where New Position is identified with w fc+1 ; Old Position is identified with w k ; Velocity is identified with [cum - j; and Elapsed Time is identified with ⁇ .
  • the ⁇ CURE update functions as an averager rather than a predictor, putting weight (1- ⁇ ) on the current weight vector w ⁇ and weight ⁇ on cum which is an estimate of the generalized steering vector a k+l (or its projection b A+1 ).
  • the tip of cum would lie to the right of w* +1
  • the scaled difference vector ⁇ [cum - j would point to, but not pass through, cum.
  • FIG. 20 similarly shows the vector relations for one cycle of the ⁇ CURE update when the iteration is performed in the P dimensions of the signal subspace.
  • the vectors are similar to those in FIG. 19 with the key difference being that b k is the projection of the generalized steering vector a k into the signal subspace, and w k+l approximates b k+l , the projection of a k+i .
  • the estimated generalized steering vector may be obtained by backprojecting the terminal weight vector w into the M dimensional sensor space.
  • ⁇ - ⁇ and ⁇ iterative updating has an advantage in the situation where signal sources are persistent but moving (i.e., non-static geometry).
  • CiCURE, eCURE, and pipeCURE are formulated in the batch-processing mode (i.e. , array snapshots are processed one block of samples at a time) assuming the source geometry is static during a block.
  • - ⁇ CURE and ⁇ CURE accommodate greater changes from block to block by providing for tracking of the generalized steering vectors via the ⁇ - ⁇ tracking method, which is well known in the sonar and radar engineering literature.
  • ⁇ - ⁇ CURE and ⁇ iterative updating have tracking capability inherently built in which can be used to improve the performance of CiCURE, eCURE, and pipeCURE in situations in which EGSVs are changing dynamically.
  • This tracking capability enables the adaptation to geometrical changes that occur gradually over time. Abrupt changes, like the appearance of new signals or disappearance of old signals, and attendant port switching are a different problem.
  • Detection logic is still required to mitigate port switching caused by abrupt changes.
  • this section presents a method for separating signals that is non-iterative. It is, in fact, a closed form, analytic solution for computing the cumulant vectors and generalized steering vectors without the need for iteration. Because the method is non-iterative, the issues of convergence and convergence rate are no longer of concern. Convergence is both assured and instantaneous.
  • the generalized steering vectors for a small number of sources are computed directly as set forth below, using one of two computational methods:
  • V l l V ll C l V l+ C 2 V 2)+hl (cs ! +C 5 V 2 )+V * V 2 (c 2 V 1 +C 4 2 )+V2V 1 (c 2 V ⁇ + ⁇ 3 V 2 ) v 2
  • Waveform continuity can be implemented as described in Section 5.0 (pipeCURE).
  • the principal eigenvector can be used to separate sources: assume e t is the principal eigenvector of F, and let its components are defined as:
  • the second source waveform can be estimated using: - ⁇ .
  • null eigenvector can be used to separate sources together with a solution of a quadratic equation: let x denote the 3 by 1 vector that is orthogonal to the columns of of F (which can be obtained using SVD or QR decomposition of F):
  • the present invention is able to operate under such overload conditions.
  • Cochannel signal separation systems are designed to be able to separate and recover signals provided the number of cochannel signals incident on the array does not exceed a number that defines the separation capacity of the system.
  • the separation capacity is equal to the number of sensors M in the receiving array. Consequently, a cochannel signal separation system based upon this method can have no more than M output ports.
  • the number of output ports can be less than M.
  • a system can have P output ports, where P ⁇ M. In this case the system could recover each of P signals from among M signals incident on the array. Each of the P signals is recovered by a different set of beamformer weights.
  • Each such set or weight vector defines a sensor directivity pattern having M-l nulls.
  • the cumulant optimization or iteration which is the basis of the invention, still converges to a generalized steering vector of a signal or noncoherent multipath group. Consequently, the system of the invention determines up to M generalized steering vectors from which beamforming weights are computed and which can form up to M-1 generalized nulls.
  • the number of signals is greater than capacity, it is not possible to recover a given signal while simultaneously rejecting all other signals by means of generalized nulls.
  • P-M excess signals or G-M noncoherent groups
  • These excess signals are rejected merely by the sidelobe suppression associated with each beamformer's directivity pattern.
  • the signals that are captured for recovery by the system tend to be the strongest signals, while the excess signals tend to be among the weakest.
  • the excess signals contribute to the noise floor of the output ports. However, their contribution is minimal because of their low relative power and the sidelobe attenuation. Consequently, the recovered signals at the output ports generally have low crosstalk levels and high signal-to- interference-plus-noise ratio (SINR).
  • SINR signal-to- interference-plus-noise ratio
  • FIGS. 21 and 22 illustrate the overload concept diagrammatically.
  • FIG. 21 shows a basestation having a four-element antenna array, which provides input signals to a CURE system for separating received cochannel signals. Because the antenna array has only four elements, the system has a capacity of four channels. The figure also shows five users, designated User A, User B, User C, User D and User E, all attempting to transmit signals to the basestation array.
  • FIG. 22 shows a directivity pattern associated with the antenna array as conditioned by the CURE system to receive signals from User A. Because the array has only four elements, it can present directivity nulls in only three directions.
  • the directivity pattern presents a strong lobe in the direction of User A, to receive its signals, and presents its three available nulls toward User B, User D and User E.
  • User C which produces the weakest and most distant signal, cannot be completely nulled out. Similar directivity patterns will be generated for receiving signals from User B, User D and User E. In each case, the weakest signal (from User C) will not be completely nulled out by the directivity pattern. The system continues to operate, however, and is degraded only in the sense that weaker sources exceeding the system capacity cannot be recovered and will produce some degree of interference with the signals that are recovered.
  • the various embodiments of the invention are tolerant of separation capacity (or number-of-signals) overload conditions.
  • the various embodiments of the invention are "failsoft" with respect to overload beyond the signal separation capacities, and the ability to separate signals degrades gracefully as the number of signals is increased above the separation capacity.
  • This property distinguishes CURE algorithms from DF-beamforming cochannel signal copy algorithms, such as MUSIC and ESPRIT , which do not function when overloaded.
  • MUSIC is an acronym for Multiple Signal Identification and Classification
  • ESPRIT is an acronym for Estimation of Signal Parameters via Rotational Invariance Techniques.
  • Multipath propagation occurs when a signal from a source travels by two or more distinct paths to arrive at a receiving antenna from several directions simultaneously.
  • a novel feature of the present invention is its ability to separate cochannel signals in a multipath signal environment.
  • Multipath propagation is caused by the physical processes of reflection and refraction. A similar effect is caused by repeater jamming, wherein a signal is received and retransmitted at high power on the same frequency.
  • Repeaters are commonly used in radio communication to fill in shadow zones, such as around hills or inside tunnels, where the communication signal does not propagate naturally.
  • Cochannel repeaters are also used in electronic warfare (EW) systems to "spoof" a radar system by retransmitting a radar signal with a random delay at sufficiently high power to mask the actual radar return. The amount of delay is set in order to cause a false distance to be measured by the radar.
  • EW electronic warfare
  • Naturally occurring multipath propagation can consist of a small number of discrete specular reflections, or it can consist of a continuum of reflections caused by scattering from an extended object.
  • the various multipath components arriving at the receiving antenna will generally be somewhat different. Differences among the various multipath components of a given source signal are (1) different directions of arrival (DO As); (2) different time delays due to the different path lengths traveled; and (3) different Doppler shifts on each multipath component due to motion of the transmit antenna, receive antenna, or reflecting body.
  • Array-based cochannel signal separation and recovery systems traditionally have difficulty working in a multipath environment (i.e., when the one or more of the arriving signals incident on the array come from several distinct directions simultaneously). For example, most multiple source DF-beamforming signal copy systems generally do not work properly or well in a multipath environment, and special techniques such as spatial smoothing and temporal smoothing must be employed to DF on the individual multipath components of an arriving signal. The resultant system and processing complexities make DF-based cochannel signal recovery systems largely impractical for reception in signal environments characterized by significant multipath propagation.
  • the cochannel signal separation capability of the CURE family of systems overcomes these limitations. To understand how the CURE system behaves in the multipath context requires understanding the different types of multipath effects and how the system handles each type.
  • multipath arrivals of a source signal can be classified as either coherent or noncoherent depending on whether the arrivals' cross-correlation function computed over a finite time interval is large or small.
  • coherent or noncoherent is relative to the length of the measurement interval.
  • Coherent multipath components frequently occur in situations where the scatterering bodies are near either the transmit or receive antennas and are geometrically fixed or moving at low velocities.
  • Noncoherent multipath is caused by path delay differences and Doppler shift differences that are large compared to the measurement window.
  • the CURE algorithms recover generalized steering vectors as opposed to ordinary steering vectors.
  • An ordinary steering vector is the value of the array manifold at a single angle corresponding to a source's DOA.
  • the received wavefield that the array spatially samples is composed of many plane waves for each source signal. Each source, therefore cannot be characterized by a single DOA or steering vector.
  • the CURE system finds a single steering vector for each independent signal source.
  • these steering vectors do not correspond to the ordinary steering vectors, which could be innumerable in the presence of many multipath scatterers.
  • the CURE vectors are generalized steering vectors that correspond to the sum of all the mutually coherent multipath components of a signal source incident on the array.
  • the generalized steering vector depends on the relative power levels, phases, and ordinary steering vectors of the multipath components.
  • the CURE signal recovery process is blind to the array manifold.
  • the beamforming weight vectors for signal recovery are computed directly from the generalized steering vectors. This is done by one of two methods: (1) by projecting each generalized steering vector into the orthogonal complement of the subspace defined by the span of the vectors of the other sources (by matrix transformation using the Moore-Penrose pseudo- inverse matrix); or (2) by using the Capon beamformer, also called the Minimum Variance Distortionless Response (MVDR) beamformer in the acoustics literature, to determine the recovery weight vectors from the generalized steering vectors.
  • MVDR Minimum Variance Distortionless Response
  • Each output port of the CURE-based system has a beamforming weight vector that is orthogonal or nearly orthogonal to the generalized steering vectors of the cochannel signals that are rejected by the output port.
  • Each beamforming weight vector has a corresponding directivity pattern that assigns a gain and phase to every possible direction of arrival.
  • These directivity patterns can have up to N-l nulls, where N is the number of array elements.
  • the nulls can be either physical nulls in prescribed directions-of-arrival (DOAs) or, in the case of coherent multipath, generalized nulls.
  • DOAs directions-of-arrival
  • Generalized nulls are not directional nulls but rather are formed when a directivity pattern assigns gains and phases in the directions of the coherent multipath components of an interfering signal such that the components sum to zero.
  • Generalized nulls have a major advantage over physical nulls for combating cochannel interference from coherent multipath because fewer degrees of freedom (i.e., fewer array elements) are required to cause coherent multipath components to sum to zero than are required to directionally null each component separately.
  • FIG. 23 illustrates these concepts for the case of a transmission received over a single-bounce path, designated multipath arrival A, a direct path, designated multipath arrival B, and a two-bounce path, designated multipath arrival C.
  • the three multipath components are indicated as having steering vectors of amplitude and angle combinations A, Z ⁇ , A 2 Z ⁇ , and A 3 Z ⁇ , respectively.
  • the CURE signal recovery system of the invention presents a directivity pattern that assigns a gain and phase to every possible direction of arrival.
  • the gains and phases corresponding to the three multipath components in this example are shown as D, Z ⁇ ; , D 2 Z ⁇ j> 2 , and D 3 Z ⁇ 3 .
  • Multipath arrivals of a source signal are not always coherent and capable of being combined.
  • the coherency requirement for CURE is that the multipath components of a signal must have high cross-correlation computed over the duration of a processing block or data collection interval. Multipath coherency can be destroyed by large path delay differences and large Doppler shift.
  • the CURE algorithms recognize and treat the arrivals as independent cochannel signals.
  • the steering vector is estimated for each arrival, and each arrival is separately recovered and assigned to a different output port.
  • multiple recovered versions of the source signal are formed. It is straightforward to recognize a noncoherent multipath situation because the same signal will be coming out of two or more output ports, each with a slightly different time delay or frequency offset.
  • FIG. 24 shows the sensor array complex directivity pattern in a situation involving receipt of two coherent multipath components of a desired signal and a noncoherent signal from an interference source.
  • the complex directivity pattern includes a null presented toward the interference source, while the two multipath components are received and combined in the same way as discussed above with reference to FIG. 23.
  • the CURE algorithms automatically partition the multipath arrivals into mutually coherent groups, and determine a generalized steering vector for each group.
  • multiple recovered versions of the source signal are formed.
  • the diversity gain is diminished relative to what would have been achieved had the multipath arrivals all belonged to a single coherent group.
  • the loss of diversity gain is offset by having multiple replicas of the recovered signal appear at the beamformer's outputs.
  • a post-recovery combining gain is possible by adding the signals at the output ports after correction for delay and Doppler shift. If the recovered signals are to be demodulated, this post-recovery combining step would precede demodulation.
  • FIG. 25 shows the complex directivity pattern formed by the beamformer of the CURE system in a situation similar to that discussed above for FIG. 24, except that the desired signal multipath components are non-coherent instead of coherent.
  • the CURE system treats the three arriving signals (the non-coherent multipath components and the interfering signal) as being from separate sources.
  • the directivity pattern shown is the one that would be presented for recovery of the multipath component designated Arrival B. Physical nulls are presented toward the other arriving signals.
  • FIG. 26 shows a slightly different situation, in which the received signals include a first interferor (A), a second interferor (B) having two coherent multipath components, and a desired signal.
  • Interferor A is rejected by a physical null in the complex directivity pattern.
  • Interferor B is rejected by a generalized null in the directivity pattern, such that the algebraic sum of the multipath arrivals of the signal from Interferor B is zero. If the Interferor B signal arrivals are characterized by gains and phases A, Z ⁇ and A 2 Z ⁇ , and the directivity pattern at the angles of arrival of these components has gains and angles D, Z ⁇ , and D 2 Z ⁇ 2 , then the necessary condition for rejection of the Interferor B signals by the generalized null is:
  • the apparatus of the invention includes an antenna array and a cumulant recovery (CURE) processing system, which processes signals received through the antenna array and produces outputs at multiple ports corresponding to the multiple sources from which signals are received at the antenna array.
  • CURE cumulant recovery
  • an interfering transmitter may be "friendly,” that is to say operated necessarily near a radio receiver, or even on the same ship or vehicle. Even if the transmitter is operating on a different frequency, there is often “spectral spatter” into the receiving band.
  • the interfering transmitter is not “friendly” and is much more powerful than the communication signals to be received and recovered.
  • the second case where a strong jamming signal is not under
  • the present invention pertains to systems for recovering communication signals in the presence of interfering or jamming signals, whether or not on the same frequency. More specifically, as shown in FIG. 27, a receiving antenna 280 may be located on the same vehicle or vessel as a local transmitter 282, and there may be a high-powered transmitter 284 located on a nearby friendly vessel operating at the same or a different frequency.
  • a desired signal is received from another transmitter 106, located on land or on another vessel, but is subject to interference from the high-powered transmitter 284 and from the local transmitter 282.
  • the receiving antenna 280 is coupled to a cumulant recovery (CURE) processing system 290, which rapidly processes the signals from the antenna array 280 and generates outputs on multiple ports, effectively separating the signals received from the high-powered interfering transmitters and the desired received signal onto separate output channels, as indicated for ports #1, #2 and #3.
  • CURE cumulant recovery
  • a related situation is one in which reception at the receiving antenna 280 is interfered with by a high-powered jamming transmitter 292, perhaps on an enemy vessel.
  • the CURE processing system 290 separates and recovers the desired weak signal on one output port (#2), while the jamming signal is isolated and may be discarded from port #1.
  • the blind cochannel signal separation capability of the CURE algorithms can be used to make possible a new communication channel access scheme: Diversity Path Multiple Access (DPMA).
  • DPMA Diversity Path Multiple Access
  • CDMA Code division multiplexing/multiple access
  • FHM/FHMA Frequency hop multiplexing/multiple access
  • ADM/ADMA Angle division multiplexing/multiple access
  • FDMA frequency division multiple access
  • FDMA frequency division multiple access
  • FDMA was historically the first multiplexing/multiple access method to develop. Its origin is traced back to the beginning of radio, and it is the basis for radio and television broadcast services, whereby an individual is able to receive and select among the signals transmitted by many stations.
  • the transmitting users signals are not cochannel, and cochannel interference is thereby avoided.
  • the remaining five channel access schemes enable two or more users to be on the same frequency at the same time (i.e. , user transmitted signals can be cochannel). The schemes mitigate or prevent mutual interference by different means.
  • SDMA is the cellular concept, which originated at Bell Laboratories (The Bell System Technical lournal, special issue on Advanced Mobile Phone Service, vol. 58, no. 1, January 1979). Users are divided geographically into cells, seven of which are indicated at 300 in FIG. 29. Each cell 300 has a base station 302, and the base stations are linked together via fixed land lines 304 or point to point microwave links.
  • PSTN public switched telephone network
  • the set of base stations 302 and the MTSO 306 form the "backbone" infrastructure of the mobile network.
  • Each base station 302 has a finite set of frequencies for sending and receiving, and adjacent cells 300 have different sets of frequencies. Within each cell, FDMA is employed to prevent cochannel interference. There are only a finite number of frequency sets available, and base stations 302 that are separated by some minimum distance use the same frequency sets. Thus, two transmitting users, indicated at 310 that are on the same frequency are necessarily in different cells some distance apart. Each users signal enters the backbone through a different base station. The geographic distance between cells prevents cochannel interference.
  • SHMA prevents cochannel interference by prohibiting intracell frequency reuse and allowing only intercell frequency reuse.
  • the remaining four channel access schemes TDMA, CDMA, FHMA, and ADMA overcome this restriction and enable frequency reuse among users within a cell-intracell frequency reuse.
  • TDMA Time Division Multiple Access
  • All users transmit on the same frequency.
  • Each transmitting user is assigned a unique time slot in which to transmit.
  • the average rate of information transmission equals the peak or instantaneous rate times a duty factor which is the slot duration divided by the revisit interval.
  • CDMA is a form of direct sequence spread spectrum in which the various users encode their transmissions with orthogonal or nearly orthogonal spreading sequences. All transmitting users use the same frequency. In order to receive a particular signal, a receiver must despread the signal using the same sequence that was used to spread it at the transmitter. Because of the orthogonality property, the cross-correlation between any two spreading codes is near zero. For this reason, the user signals after reception and despreading are free of cochannel interference. CDMA is the basis of the IS-95 communication standard. FHMA is used to apply frequency hop spread spectrum technology to communication networks.
  • a set of frequency hopping (FH) radios operate in the same band on the same hop frequencies and transmit to a central receiving facility or base station without mutual interference provided the radios use non-interfering hop sequences.
  • the required sequence property is not orthogonality or low cross-correlation, but rather a mathematical relative of the Latin Square.
  • FHMA can be thought of as a dynamic form of FDMA in which the frequency assignments change regularly.
  • ADMA which is shown in FIG. 30, uses multi-source direction finding (DF) and beamforming technology to isolate and recover the signals from the transmitting cochannel users in a cell.
  • Each base station 302 is equipped with a receiving array connected to an N-channel receiver (not shown), where N is the number of antennas in the receive array.
  • the received signals are processed by a multi-source DF system 312 to determine the directions or angles of arrival (DOAs) of the signals on a given frequency.
  • Any multi-source DF algorithm can be used to perform the DF function, such as MUSIC, ESPRIT, or WSF, all of which are well known in the signal processing engineering literature.
  • Each user 310 is characterized by a single unique DOA.
  • Beamforming weight vectors are then computed, as indicated in block 314, from the estimated directions that enable the cochannel signals to be recovered (separated and copied).
  • a transformation matrix whose rows are the beamforming weight vectors, multiplies the array signals, and the product yields the recovered cochannel signals.
  • Each row of the transformation matrix i.e., each weight vector
  • the transmitted user signals can be recovered free of cochannel interference provided the users are angularly dispersed such that they have distinct bearing angles measured at the receive array.
  • ADMA is described in a recent patent by Roy and Ottersten (Richard. H. Roy, III, and
  • the present invention achieves a new method for channel access in wireless communications that is distinct from the six basic methods described above.
  • the new method is termed diversity path multiple access (DPMA). It overcomes three limitations of ADM A.
  • wireless channels characterized by their angle spread, delay spread, and Doppler spread
  • the transmitting user signals arrive at a base station from a multiplicity of directions simultaneously.
  • Angle spread arises due to multipath from local scatterers and remote scatterers.
  • the local scatterers are near the user and near the base station. Measurements have shown that angle spreads for cellular channels generally lie in the range from 2 to 360 degrees.
  • the ADMA concept of a single wave arriving from a single direction characterized by a pair of angles for each cochannel signal source is valid in free-space communications, perhaps, but is not valid for wireless communication networks operating in the ultra high frequency (UHF) band in urban, suburban, or rural environments.
  • UHF ultra high frequency
  • ADMA requires the transmitting cochannel users DO As to be distinct. That is, the angular separation between users cannot be zero. The users must be separated in angle from one another by some minimum angle. The assignment of a frequency to several cochannel users must take this geometric restriction into account. This limits the utility of ADMA.
  • CURE algorithms recover generalized steering vectors as opposed to ordinary steering vectors.
  • An ordinary steering vector is the value of the array manifold at a single angle corresponding to a source's DOA.
  • the wavefield at the receiving array is composed of more than one plane wave for each source signal. Consequently, sources cannot be characterized unique DOAs or steering vectors.
  • the CURE signal recovery process is blind to the array manifold.
  • each generalized steering vector is a complex weighted sum of the array manifold steering vectors at the multipath arrival directions. The complex weights account for path length and attenuation differences among the multipath arrivals.
  • the multipath structure consists of a continuum rather than a few discrete components. In such cases the generalized steering vector becomes an integral of the array manifold over all directions.
  • the CURE system determines the generalized steering vectors directly from the received signals, not from the array manifold. Indeed, the various embodiments of the CURE system do not need the array manifold to perform signal separation and recovery.
  • the beamforming weight vectors for signal recovery are computed directly from the generalized steering vectors. This is done by one of two methods: (1) projecting each generalized steering vector into the orthogonal complement of subspace or span of the vectors of the other sources; (2) using the minimum variance distortionless beamformer (MVDR) equations to determine the recovery weight vectors from the generalized steering vectors.
  • MVDR minimum variance distortionless beamformer
  • the former solution would provide zero crosstalk (i.e. , maximum signal-to-interference ratio) among the recovered signals at the beamformer output.
  • the latter solution would provide recovered signals having maximum signal-to-interference-plus-noise ratio (SINR).
  • FIG. 31 shows a single cell 300, with a basestation 302 and two users 310.
  • One user (A) reaches the basestation through multipath propagation, while the other has a direct propagation path to the basestation.
  • a CURE processing system 316 receives and processes the signals received by the basestation 302. In communicating with cochannel user A the system 316 generates a beamformer directivity pattern that presents a physical null toward the other user, but presents a generalized steering vector that results in both multipath components from cochannel user A being received and combined.
  • a feature of the CURE systems is that the omnipresence of multipath enables the recovery (separation and copy) of signals from sources that have zero angular separation from the point of view of the receiving base station array. For example, consider two sources that are collinear with the base station such that one source lies behind the other. Although the direction to both sources is identical and the ordinary steering vectors for line of sight propagation are identical, the multipath configurations are entirely different. Therefore, the generalized steering vectors of the two sources will be entirely different. This facilitates the separation and recovery of the cochannel source signals in situations where ADMA cannot work.
  • the CURE method realizes a diversity gain in the presence of multipath.
  • the amount of the gain depends on the number of distinct multipaths that are sufficiently coherent to be able to be combined. Not all multipath components of a source will be coherent and capable of being combined.
  • the coherency requirement is that the multipath components of a signal must have high cross-correlation computed over the duration of a processing block or data collection interval. Multipath coherency can be destroyed by large path delay differences and large Doppler shift.
  • the CURE algorithms automatically partition the multipath arrivals into coherent groupings, and determine a generalized steering vector for each group.
  • DPMA a new communication network channel access method
  • the communication path that defines the link from a transmitting user to a receiving base station consists of a weighted combination of multipaths.
  • the multipath processing capability of the CURE systems provide a practical means for implementing a communication network employing DPMA.
  • the CURE algorithms determine the complex multipath-combining weights of a desired signal automatically, dynamically, and in real time, while rejecting the multipaths of cochannel other-user signals.
  • the CURE cochannel signal separation technology is applicable to communication networks composed of two-way communication links in which multiple transmissions occur simultaneously on the same frequency.
  • the natural application for CURE is to the receiving end of a communication link provided it is practical to have an antenna array at the receiving end. For a two-way communication link, this would mean having a receiving antenna array at both ends of the link. In many situations, however, it is practical to have an array at only one end of a communication link. For instance, in personal mobile wireless communication networks, it is impractical to have an array built into the user's portable handheld units. In such situations, it is possible, under certain conditions, to establish and maintain isolation between different cochannel users by putting arrays at just one end of the communication link.
  • CURE cochannel signal separation technology can be applied to cellular commumcation systems in which the earth's surface is partitioned into localized regions called 'cells,' as described above with reference to FIG. 29.
  • Examples of cellular personal mobile wireless commumcation systems are the Advanced Mobile Phone System (AMPS) and Global System Mobile (GSM). (See The Bell System Technical Journal, special issue on Advanced Mobile Phone Service, vol. 58, no. 1, January 1979.)
  • the CURE cochannel system has been described to this point as a technology used at the receiving ends of communication links provided the receiving ends have multi-element antenna arrays. In the case of cellular networks, however, economics dictates that arrays be put at base stations only. There are two reasons for this:
  • An antenna array is a large and expensive physical asset, best suited to installation at fixed base stations where proper maintenance and repair is possible. Mobile units would be larger and more expensive if they were required to have antenna arrays built in.
  • a single base station array can serve many users at once. Since each cell has more mobile units than base stations, total system cost is lower if arrays are employed at the base stations only.
  • the effect of locating arrays at base stations is to lower the initial equipment purchase cost to the subscribers while increasing the infrastructure cost, which is spread over all subscribers in the form of monthly service charge.
  • CURE cochannel signal separation technology can be applied to satellite-based personal communication networks in which a space-based array on a satellite forms spot beams on the surface of the earth that define regions similar to the cells formed with terrestrial base stations. All communication within a spot beam is between the mobile users and the satellite. Communication that bypasses the satellite, between two users in the same spot beam, is precluded. Examples of satellite-based personal mobile wireless communication systems are Iridium, Odyssey, and Global Star.
  • CURE cochannel signal separation technology there are several reasons and advantages relative to the use of CURE cochannel signal separation technology in personal mobile wireless communication networks. • The capacity of a network to accommodate users can be increased by employing intra-cell frequency reuse. CURE technology makes this possible by means of diversity path multiple access (DPMA) on the reverse links or uplinks from mobile user-to-base stations or satellite and by transmit beamforming on the forward links or downlinks from base station or satellite to mobile user.
  • DPMA diversity path multiple access
  • CURE Apart from capacity improvement, CURE provides diversity gain which, when used with suitable power control algorithms, can enable the mobile users to maintain reliable communication with less average transmitted power. • CURE provides general interference immunity not only from other users in the network but from arbitrary radiated interference, whether deliberate or unintentional.
  • the selectivity that enables several users to simultaneously share a radio-frequency (RF) channel for transmission can be accomplished by beamforming at the transmitters instead of at the receivers.
  • RF radio-frequency
  • Two methods can be used to accomplish the requisite transmit beamforming function: switched fixed beams and adaptive beams.
  • the basic principles of both approaches are known in communication engineering. However, proper operation depends on integration with the receive beamforming function provided by the CURE system. This integration is described below.
  • a transmit antenna array and a set of fixed pre-formed beams is available for transmission.
  • the beams are formed by applying signals with appropriate gains and phases to the antennas.
  • the gains and phases can be created either by a passive beamforming matrix that is inserted into the signal path ahead of the antenna array.
  • the outputs of the beamforming matrices are then summed in power combiners that drive each array antenna.
  • the preferred approach shown in FIG. 32, eliminates the expense of RF hardware beamformers in favor of digital signal processing.
  • a signal to be transmitted to a user is input on one of N multiple lines 320 to one of N sets of multipliers 322.
  • Each set of multipliers has as other inputs a transmit beamformer weight vector, which is derived from a transmit/ receive beamformer weight vector computer 324.
  • the latter computer receives estimated generalized steering vectors on lines 36 from the CURE system and generates receive beamformer weights on lines 48 (see FIG. 4A), and transmit beamformer weight vectors on lines 326.
  • a transmit beamformer weight vector which is an M-dimensional complex weight vector, where M is the number of antenna elements.
  • the outputs of the multipliers 322 are summed in a plurality M of summers 328.
  • each summer 328 sums the contributions of multiple user signals associated with a particular antenna element.
  • the signal to be radiated by the I th antenna of M elements is the sum of N terms, each being a complex weighted version of the signal to a different user.
  • An M-channel digital-to-analog converter (DAC) 330 and linear power amplifier (LPA) 332 is used to drive each antenna. This latter method does not require expensive analog rf beamforming matrices and power combiner hardware, since the multipliers 322 and summers 328 are digital processing components, as indicated by the envelope 334.
  • the beamforming weight vectors are pre- computed and stored in memory. Each weight vector can be used to create a directional beam that puts transmitted energy in a different direction.
  • the set of all such weight vectors provides a family of pencil beams that covers all directions in the cell. Only one such beam is selected for transmission on each of the L forward links.
  • the method of selection is described below. The method mitigates, but does not eliminate, cochannel interference because the energy of a signal unintentionally radiated to other cochannel users is suppressed to the sidelobe level of the beam, assuming the other users do not fall into the main lobe of the beam.
  • FM analog frequency modulation
  • a beam is chosen for transmission to a particular user by means of logical rules embodied in a beam selection algorithm.
  • the objective is to prevent energy from reaching the other cochannel users where it would interfere with the intended signals being sent to those users.
  • the transmit and receive arrays are geometrically similar (i.e., have the same shape).
  • the transmit and receive arrays have the same size-frequency products (i.e. , the ratio of the transmit-to-receive array sizes equals the ratio of the receive- to-transmit frequencies).
  • the transmit and receive arrays are mounted on a common vertical mast.
  • the dominant multipath scatterers are not in the immediate vicinity of the arrays, so that the arrays are in the farfield of reradiation from scatterers, and the elevation or depression angle of arrival is essentially zero at both arrays.
  • the best beam for sending energy to a given user is the one whose beamforming vector is most nearly orthogonal to the generalized steering vectors of the other cochannel users (the generalized steering vectors being those derived from reception of the reverse link signals at the base station).
  • Orthogonality between two vectors is strictly defined as an inner product of zero. However, strict orthogonality is not generally possible. Fortunately, it is often good enough to pick the beam whose weight vector has the smallest inner product with the reverse link generalized steering vectors of the other cochannel users. This beam will radiate minimal sidelobe energy to the other users.
  • the beam selection criterion for using a fixed switched-beam array can be stated precisely: Choose the beam that maximizes the ratio of the inner product of the beam vector with the generalized steering vector of the intended user divided by the sum of the inner products with the generalized steering vectors of the unintended cochannel users.
  • the method just described uses a fully adaptive array for the reverse link receive function, as implemented by the CURE method, together with a switched- beam array for the forward link transmit function.
  • the key feature of this approach is that antenna arrays are employed only at base stations.
  • a somewhat different approach would be to perform the transmit beamforming by using the exact generalized steering vectors as are derived by the receive function.
  • This method requires a transmit array that is geometrically similar to the receive array as described above (i.e., the transmit and receive arrays have the same shape but are scaled by the ratio of the receive-to-transmit frequencies). For example, in the case of the AMPS analog cellular systems, the transmit and receive frequencies are offset by 45 MHz.
  • the 45 MHz offset can be regarded approximately as a 5 percent difference in scale.
  • a scaled transmit array if a generalized steering vector obtained by CURE on receive is used for transmit, then the same array directivity pattern will result. Thus, lobes and nulls will be placed at the same angles. Nulls directed to other cochannel users on receive will also be directed at the same other users on transmit, thereby enabling the base station to selectively direct a signal at a particular desired cochannel user.
  • the desired user and other user signals will generally be via diversity paths (i.e., the DPMA concept).
  • the generalized steering vectors derived by CURE on receive cause the receive array (and hence the transmit array) to have a complex directivity pattern for each user that sums the multipath arrivals of the desired signal with complex weights (gains and phases) that causes them to add in phase, while simultaneously summing the multipath arrivals of each other user cochannel signal with complex weights that causes these signals to sum to zero.
  • other users are rejected by generalized nulls or orthogonality rather than by physical nulls at specific angles.
  • the energy transmitted to a particular user will be sent in the same direction as the receive multipath components, with the same phase and gain relationships. Therefore, the signal will reach the intended user with a substantial signal level via the diversity path. Simultaneously, the signal will reach the other users via multiple paths that will sum to zero provided the mobile users are using a simple omnidirectional antenna for both transmit and receive.
  • FIG. 33 is block diagram of a transmitter for use in one form of the CURE system. Some of the components of the transmitter have already been introduced in the discussion of FIG. 32.
  • a transmit weight vector computation or beam selection module 340 generates on lines 326 a transmit weight vector for each user k. the module 340 generates the transmit weight vector based on either of the two approaches discussed above
  • An information signal to be transmitted to user k is modulated in a modulator 342 and then multiplied by the transmit weight vector for user k, in a set of multipliers 328.
  • the portion of the transmitter including the modulator 342 and multipliers 322 is referred to as the transmit beamformer 344.
  • each summer 322 receives as inputs an antenna element contribution associated with each of the users.
  • each summer 322 has N inputs if there are N users.
  • the summers 328 are collectively referred to as a signal combiner 346.
  • the outputs of the summers 328 are then processed in what is referred to as the air interface 348 of the transmitter.
  • the air interface includes a set of complex digital-to-analog converters (DACs), each of which produces two outputs, the in-phase and quadrature components of the complex signals.
  • DACs complex digital-to-analog converters
  • These complex signal components are multiplied by a carrier signal in additional pairs of multipliers More specifically, each complex output pair from a complex DAC 330 is multiplied by signals proportional to cos ⁇ c t and sin ⁇ c t, respectively, where ⁇ c is the angular carrier frequency.
  • the resulting products in each pair are then added in summers 352 and coupled to one of the linear power amplifiers 332, and from there the signals are coupled to an antenna element 110.
  • This section describes a method and apparatus for mitigating polarization effects on propagated radio signals.
  • the effects of a polarization-changing propagation medium are avoided by separating the two received signals without regard to their polarization states.
  • This invention relates generally to radio communications and, more specifically, to problems that arise, due to natural propagation conditions, when multiple cochannel signals of practically the same frequency are received at approximately the same time. Propagation conditions may cause may cause unwanted polarization mixing of the signals. Separating and recovering the original signals poses difficulties in receiver design.
  • a related problem is multipath propagation caused by reflections from atmospheric layers, such as the D-layer, E-layer or F-layer.
  • the problem manifests itself as frequency selective fading or phase distortion that limits the communication capability of high-frequency (HF) signals.
  • HF high-frequency
  • electromagnetic propagating waves are used to carry two independent information signals on different polarizations of the same carrier signal. These polarizations need not be orthogonal, but do need to be linearly independent relative to two orthogonal "basis" polarizations, e.g. vertical and horizontal linear polarizations or left-hand circular and right-hand circular polarization.
  • a traditional problem is that the polarization of a transmitted signal is changed by the propagation medium so that the signal arrives at the receiving antenna with a different polarization from the one in which it was transmitted. The polarization change may be due to reflection from oblique surfaces, refraction, or the phenomenon of Faraday rotation.
  • Conventional receivers separate differently polarized signals because each receiver has knowledge of the expected polarization states.
  • the conventional receiver When the polarization of one or both signals is changed during propagation, the conventional receiver is incapable of properly separating the two signals.
  • the present invention separates the received signals without regard to their possibly changed polarization states. If only one signal is received with an unknown polarization is received at a dual-polarized antenna, the invention can extract the signal and determine its polarization state. If two signals are sent on orthogonal polarizations, the signal polarizations can be random and not orthogonal at the receiving site, making reception of either signal subject to cochannel interference from the other signal.
  • CURE processing solves the problem by separating and recovering up to two independent signals arriving at the receiving array with differently polarizations, provided only that the polarizations are linearly independent (i.e., not identical).
  • the key advantage of the invention in this application is that it is "blind" to the polarization states of the received signals. Prior knowledge of the polarization state is not needed to separate and recover the signals. In addition, the CURE approach is fast enough to enable the recovery of signals whose polarization is time-varying.
  • one type of communication system makes use of dual-polarized signals at the same frequency.
  • transmitters 360A and 360B transmit uplink signals A and B to a commumcation satellite 362, which retransmits the signals, with different polarization states, to a dual-polarized antenna 364 on the ground at a receiving site.
  • an atmospheric layer 366 causes polarization mixing of the two signals, which arrive at the receiving antenna with scrambled polarization states.
  • the received signals are processed by a CURE processing system 368, which effectively separates out the signals A and B without regard to their scrambled polarization states. Because of the CURE processing system 368 is "blind" to antenna configuration, and to the polarization state of the received signals, separation and recovery of signals A and B can be effected even when both have their polarization states altered during propagation from the satellite transmitter.
  • waveguide As mentioned earlier, the term "waveguide” as used in this specification is intended to include any bounded transmission medium, such as a waveguide operating at microwave frequencies, an optical fiber operating at , a coaxial cable, or even twisted-pair conductors operating at lower frequencies. Regardless of the waveguide medium, the signals are received at an array of sensor probes installed in the waveguide, and are fed to a cumulant recovery (CURE) system that separates and recovers the original signals without regard to how the original propagation modes may have become scrambled as a result of transmission along the waveguide.
  • CURE cumulant recovery
  • Waveguides and optical fibers are widely used for the transmission of multiple independent cochannel signals simultaneously, by using a different propagating mode for each signal.
  • mode conversion due to kinks, bends and surface and refractive irregularities in the waveguide or fiber, a phenomenon called mode conversion occurs, and the propagated energy is converted from one mode to another during propagation along the waveguide or optical fiber medium. Over a long distance of propagation, the signals tend to become scrambled across the propagating modes.
  • a well known approach to conserving bandwidth is to employ different propagating modes for different signals of the same frequency, and to rely on the different propagating modes to effect separation of the signals as the receiving end of the waveguide or optical fiber.
  • FIG. 35 shows, by way of example, a computer network employing an optical fiber 370 and having a plurality of computer workstations 372 connected to the fiber by couplers 104, each of which couples a workstation to the fiber using a different propagation mode, but at the same optical frequency.
  • a plurality of probes 376 are also coupled to the fiber 370, providing three output signal lines for connection to a cumulant recovery (CURE) processing system 378, which generates separated signals A, B and C at its signal recovery ports.
  • CURE cumulant recovery
  • This section describes a method and apparatus for finding accurate directions of multiple radio signal sources without the need for a fully calibrated antenna array.
  • Signals from the antenna array are processed in a cumulant recovery (CURE) processing system to recover the signals and obtain estimated steering vectors for the multiple sources.
  • Signals from a subarray of antennas that are calibrated are combined with the steering vector estimates to obtain accurate directional locations for all of the sources. There need be only as few as two calibrated antenna elements in the subarray.
  • This invention relates generally to direction finding (DF) systems and, more particularly, to DF systems using arrays of radio antennas.
  • Traditional super- resolution direction finding systems require an array of N+ 1 calibrated antennas and receiving channels to resolve N source locations (directions). Maintaining large arrays of antennas in calibration adds to the cost of the system. Moreover, traditional DF systems do not always converge rapidly on the direction solutions.
  • FIG. 36 shows a direction finding system in accordance with the invention, including an array of antennas 380, only two of which are calibrated, a CURE processing system 382 and a copy-aided direction finding system 384.
  • Signals are received from multiple sources 386 at different directional locations with respect to the antenna array 380.
  • the CURE processing system 382 separates and recovers the signals from the sources 386 and outputs the recovered signals from separate output ports, as indicated at 388.
  • a byproduct of the signal recovery process is a set of steering vector estimates for the multiple sources 386.
  • the K h port provides the steering vector estimate a k (m) from its analysis of the m' h block of data "snapshots," and the steering vector from the calibration table for the bearing ⁇ is denoted as a( ⁇ ).
  • a calibration table is generated, associating every bearing angle with an antenna steering vector.
  • the dimensionality of a( ⁇ ) is equal to the number of calibrated sensors, which must be greater than or equal to two.
  • the bearing Q k of the source captured by the port k is estimated by the maximizer of the DOA spectrum:
  • This section describes a method and apparatus for extending the effective dynamic range of a radio receiving system by removing the principle products of distortion through the use of a cumulant recovery processing system.
  • a received signal of interest is separated from the products of distortion, which are independent of the signal of interest.
  • the signal is forwarded for further processing and the products of distortion are discarded, resulting in an extended dynamic range.
  • the dynamic range is a measure of the useful output of a receiver in relation to noise and other unwanted components. It is limited by the intermodulation and distortion products that result from analog and digital nonlinearities. Analog nonlinear distortion products or spurs can be generated due to signal overload or saturation of the first stage, mixer noise, and other sources. Digital systems employ analog-to-digital (A/D) converters that produce nonlinear distortion due to uniform quantization noise, A/D saturation, non-monotonicity of the A/D characteristic, sampler aperture jitter, and other physical effects. Accordingly, there has been an ongoing need for significant improvement in the dynamic range of a receiver system.
  • CURE processing system 394 shows a multichannel receiver 390, receiving signals from sources 392 through an antenna array 393 and coupling the input signals to a cumulant recovery (CURE) processing system 394.
  • the received signals after analog processing and analog-to-digital conversion, have a spectrum that includes a number of products of distortion, in the form of nonlinear spurs in the spectrum, as well as lower-level quantization noise across the entire spectrum of interest.
  • the effect of CURE processing is to separate and recover (or discard) received signals.
  • the CURE processing system provides an output port for the desired signal, and generates other outputs corresponding to the principal products of distortion, which may be discarded.
  • the resulting spectrum after CURE processing exhibits improved effective dynamic range, and contains only lower intensity spurs, and low level noise. Accordingly, the invention eliminates a number of distortion products in the receiver output and provides a desired output signal with fewer and lesser products of distortion.
  • This section describes a method and apparatus for separating and recovering data recorded on closely spaced tracks on a recording medium.
  • An array of sensors senses recorded data from multiple tracks simultaneously, and a cumulant recovery processing system separates and recovers the data from each of the multiple tracks, without crosstalk or mutual interference.
  • Use of the invention permits recording of data at much higher densities than is conventional, so that more data can be stored on a recording disk without increasing its physical size.
  • a multisensor array 402 which has individual sensor elements that can sense recorded information on more than one track simultaneously.
  • N sensors will permit separation of information from N adjacent tracks.
  • the signals from the three sensors are processed as independent cochannel signals by a cumulant recovery (CURE) processing system 404, which generates outputs on three ports, corresponding to the signals on the three tracks over which the sensor array is positioned.
  • CURE cumulant recovery
  • the invention represents a significant improvement in recording and playback techniques using high-density recording media.
  • This section describes a method and apparatus for effecting automatic phase rotation equalization of a quadrature amplitude modulated (QAM) signal received from a transmitter.
  • QAM quadrature amplitude modulated
  • QAM signals are subject to an unknown phase rotation during propagation, de-rotation or phase rotation equalization is required before the received signal can be QAM demodulated.
  • received downconverted QAM signals are subject to processing in a cumulant recovery (CURE) processing system, which recovers the originally transmitted I and Q signals and automatically provides phase rotation equalization, without knowledge of the amount of rotation.
  • CURE cumulant recovery
  • This invention relates generally to communication systems and, more particularly, to phase rotation equalization in communication systems.
  • Many communication systems use a form of modulation referred to as quadrature amplitude modulation (QAM) for transmitting digital data.
  • QAM quadrature amplitude modulation
  • the instantaneous phase and amplitude of a carrier signal represents a selected data state.
  • 16-ary QAM has sixteen distinct phase-amplitude combinations, which may be represented in a "signal constellation" diagram as sixteen points arranged on a square matrix.
  • a special case of QAM signals is the phase-shift keyed (PSK) signals for which the instantaneous phase alone represents a selected state.
  • PSK phase-shift keyed
  • 16-PSK has sixteen distinct phase selections, and can be represented as sixteen equally spaced points on the unit circle.
  • FIG. 39 shows a conventional transmitter, including a QAM modulator 410 and a transmitter 412.
  • the signal constellation diagram is as shown at 414, with sixteen phase-modulus points arranged in a square matrix. Each point on the diagram represents a unique data state.
  • a receiver and downconverter 416 generates I and Q signal components.
  • the signal constellation diagram corresponding to these signals is as shown at 418. The constellation has been rotated and must be corrected before QAM demodulation can take place.
  • the receiver site also has a cumulant recovery (CURE) processing system 420 installed between the receiver/downconverter 416 and a QAM demodulator 422.
  • CURE processing has the effect of compensating for the phase rotation induced during propagation of the signal to the receiver site, as indicated by the phase- corrected QAM signal constellation at 424.
  • the in-phase and quadrature components (I and Q) of a digital communication signal are independent and identically distributed at the transmitter output, as indicated in FIG. 40Aa). If x(t) is the original communication signal (in analytic representation) with in-phase component x p (t) and quadrature component x (t), respectively, then ⁇ (t) and x q (t) are statistically independent.
  • y(t) G exp(/ ⁇ ) .x( . If y p (t) and y q (t) denote the in-phase and quadrature components of y(t) respectively, then we can write: y P (t) cos( ⁇ ) - sin( ⁇ ) ' ⁇ * till(') '
  • the effect of transmission is to rotate the entire signal constellation by the unknown phase angle ⁇ , as is shown in FIG. 40B.
  • the constellation In order to demodulate the signal correctly, the constellation must be "de-rotated” back to its original position prior to demodulation. This de-rotation operation must be accomplished by complex phase equalization or phase correction of the I and Q signals, which compensates for the distortion introduced by the communication channel and the lack of phase reference in the receiver downconverter's local oscillator.
  • the CURE method can be applied to provide a unique solution to the problem of complex phase rotation equalization.
  • the signal's center frequency for downconversion must be known accurately enough so rotation does not occur during a processing block. The following two paragraphs help explain how the CURE system effects complex phase correction:
  • the rotation correction problem can be considered as a blind signal separation problem in which there are two sensor signals y p (t) and y q (t), each of which is a linear combination of two independent source signals x p (t) and x q (t).
  • This is precisely a problem model to which the CURE processing system can be applied.
  • the vector channel is phase equalized, the original independent I and Q signals are recovered, and the received signal constellation is de-rotated.
  • the covariance matrix used in the CURE system will be a scaled identity matrix, which simplifies the preprocessing required in the CURE system.
  • the principal advantage of using the CURE system as an equalization technique is that the rotation angle ⁇ need not be known.
  • the CURE system compensates for the angle automatically to provide independent output signals.
  • the equalization process adapts as channel conditions change.
  • the CURE system can be used to adjust the complex phase for single- dimensional constellations, such as pulse-amplitude modulation (PAM).
  • PAM pulse-amplitude modulation
  • the scenario can be considered as a two-sensor, single-source, signal enhancement problem, which can be handled by the CURE processing system.
  • the present invention is fundamentally a method for separating and recovering narrowband cochannel signals illuminating a sensorarray. However, it is possible to extend the method to the separation and recovery of wideband signals. This is accomplished by:
  • Steps 1 and 2 are straightforward. Step 3, however, is intricate and requires a special cumulant test to associate the ports at one frequency segment with the ports at adjacent frequency segments. Methods to accomplish these steps are described below.
  • the overall method is capable of separating and reconstructing wideband signals with no special constraints on the signals or their spectra other than that the components of the signals in each narrowband segment must be non-Gaussian.
  • a key advantage of this method is that the signal spectra are not required to be gap-free (i.e., have a convex support set).
  • Each narrowband component (r(t, ⁇ ) is fed to a different CURE signal separation processor 432 that separates and recovers the signals that comprise the narrowband component.
  • gk(t,fi be the waveform recovered by the ⁇ " 1 port of the CURE subsystem that operates in band / processing r(t,f).
  • r(t,f) the narrowband port signals
  • the problem of combining the recovered narrowband signals to form wideband signals is complicated primarily by the fact that the ports for different bands capture different sources.
  • the combing step is indicated as block 434 in FIG. 41.
  • the threshold is set to limit the number of false association decisions on average. For example, if port 3 of the second processor provides a minimum below threshold, then the waveforms from the two processors would be combined according to
  • d(i,k; j,l) is not a true mathematical metric because it does not satisfy two of the three required metric properties: d(i,k; j,l) ⁇ 0 and the triangle inequality are not satisfied. Nevertheless, as a pseudo-metric, it does enable port associations to be found.
  • the next step is to associate ports two at a time. This is done with a clustering algorithm borrowed from the field of statistical pattern recognition. An agglomerative hierarchical clustering algorithm is used.
  • the distances between same-band ports are used to compute the threshold that controls whether ports are sufficiently close to permit them to be merged.
  • the threshold is computed by a statistical L-estimator operating on the same- band distances. The distances are sorted into ascending order, and a particular distance is selected based on its rank. This distance is multiplied by a constant to obtain the threshold. Both the rank and the constant depend on J and L and are chosen to maintain the probability of false port association, P ⁇ ⁇ , below some small specified level (e.g., 0.001).
  • each cluster will correspond to exactly one wideband signal.
  • the final step is to recover the waveforms of the wideband signals. This is done by adding together the output port signals from all the ports merged or associated to each cluster. 20.0 Conclusion:
  • the present invention provides a cochannel signal processing and separation that has many facets.
  • Implementation of the basic cumulant recovery (CURE) processing engine may take the form of any of the proposed embodiments, including eCURE (as described in Section 3.0).
  • CiCURE (Section 4.0) or pipeCURE (Section 5.0) and its capabilities may be further extended using ⁇ - ⁇ CURE or ⁇ CURE (Section 6.0), wideband processing (Section 19.0), or direct (non-iterative) computation (Section 7.0).

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Abstract

Cette invention se rapporte à un procédé et à un appareil qui servent à traiter des signaux dans le même canal reçus au niveau d'un groupement de capteurs (110) dans un moteur de traitement et de séparation de signaux à base de cumulants, afin d'obtenir un groupe désiré de signaux de sortie (38) ou de paramètres. Pour pouvoir être utilisés dans un système de récupération de signaux, des signaux de sortie en question sont des versions séparées et récupérées des signaux transmis à l'origine dans le même canal. Une caractéristique importante qui distingue le système à base de cumulants des autres systèmes de récupération et de séparation de signaux est que le système à base de cumulants génère un vecteur d'orientation généralisé estimé qui est associé à chaque source de signaux et qui est représentatif de toutes les composantes de signal cohérentes reçues attribuables à la source en question. Cette caractéristique permet au système de cette invention de bien se comporter dans des conditions multivoies, en combinant toutes les composantes multivoies cohérentes provenant de la même source. Dans un système de récepteur/émetteur (316), les vecteurs d'orientation généralisés estimés qui sont associés à chaque source sont utilisés pour générer des vecteurs de pondération conformateurs de faisceaux de transmission qui permettent la transmission dans le même canal en direction de multiples stations d'utilisateurs (310). Ce moteur de traitement et de séparation générique à base de cumulants peut également être utilisé dans une grande variété d'applications, telles que l'enregistrement haute densité, l'égalisation d'angles de phase complexes, les systèmes de réception avec plage dynamique efficace accrue, et la séparation de signaux en présence de fortes interférences. Divers modes de réalisation et diverses extensions de ce système générique à base de cumulants sont présentés.
EP96940872A 1996-11-26 1996-11-26 Systeme de traitement de signaux dans le meme canal Withdrawn EP0948847A1 (fr)

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CA2272930C (fr) 2003-08-12
CA2272930A1 (fr) 1998-06-04
WO1998024192A1 (fr) 1998-06-04
IL130034A (en) 2003-04-10

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