CN115250153B - Digital channelization method and system for adaptive tracking filtering - Google Patents
Digital channelization method and system for adaptive tracking filtering Download PDFInfo
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Abstract
The invention discloses a digital channelizing method and a system for adaptive tracking filtering, wherein the method comprises the following steps: storing one path of a current frame of the parallel signal, and calculating frequency domain information after overlapping the other path of the current frame of the parallel signal; if a tracking channel matched with the frequency domain information exists, inputting the current frame of the parallel signal into the tracking channel, otherwise, inputting an idle tracking channel, and alternately selecting a filter by the tracking channel; the tracking channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, then the selected filter performs sliding mean filtering on the N parallel signals, and the tracking channel performs up-conversion reduction to obtain filtered signals; and splicing the signal filtered by the filter with the signal filtered by the previous filter. The invention can perform channelization on non-cooperative signals by frame and self-adaptively selecting channel parameters, and can use a plurality of narrow channels to filter and splice large-bandwidth frequency modulation signals to complete channelization.
Description
Technical Field
The invention relates to the field of electromagnetic signal processing, in particular to a digital channelization method and a digital channelization system for adaptive tracking filtering.
Background
The electromagnetic signal processing mainly has the functions of intercepting, detecting and identifying electromagnetic wave signals by using electronic equipment, and obtaining more valuable information by analyzing various parameters of the signals, thereby providing important guarantee for implementing various actions of the people. The effective reception and real-time processing of electromagnetic signals are the basis of electromagnetic signal processing, and are generally realized by adopting a broadband digital receiver.
The digital channelization technology is a core technology of a broadband digital receiver, and at present, the digital channelization technology is gradually optimized from a prototype structure after years of development, but parameters of each channel are fixed, flexible self-adaptive receiving cannot be performed on non-cooperative signals, and real-time adjustment is lacked. When the digital channelization technology is used for reconnaissance receiving of the non-cooperative electromagnetic signals, the problems of channel crossing, signal loss, poor signal-to-noise ratio improvement effect and the like of signals may occur due to poor matching degree of signal frequency bands, bandwidth and the like with channels in the receiving process of part of the non-cooperative signals.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides a digital channelizing method and a system for adaptive tracking filtering, which can be used for carrying out channelizing on non-cooperative signals frame by frame and adaptively selecting channel parameters, so that the channel is more accurate and efficient; the method can use a plurality of narrow channels to filter and splice the large-bandwidth frequency-modulated signals to complete channelization, and accordingly has a higher signal-to-noise ratio.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a digital channelization method for adaptive tracking filtering, comprising the steps of:
s1) acquiring a parallel signal, storing one path of a current frame of the parallel signal, and calculating after overlapping the other path to obtain a frequency spectrum for spectral analysisspecMaxHoldAcquiring the frequency spectrumspecMaxHoldFrequency domain information of (a);
s2) if a tracking channel matched with the frequency domain information exists, taking the tracking channel as a target channel, if the tracking channel matched with the frequency domain information does not exist, selecting an idle tracking channel as the target channel, updating channel parameters of the target channel by using the frequency domain information, inputting a current frame of a parallel signal into the target channel, updating filtering parameters of a target filter in the target channel according to the channel parameters, wherein the target channel comprises two filters, and one of the two filters is alternately selected as the target filter;
s3) the target channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, then the target filter performs sliding mean filtering on the N parallel signals, and the target channel performs up-conversion reduction to obtain the parallel signal frame filtered by the target filter;
and S4) splicing the parallel signal frame filtered by the target filter with the parallel signal frame filtered by the previous target filter of the target channel, and returning to the step S1) until the parallel signal is completely acquired.
Further, the parallel signals are six parallel signals, and the frequency spectrum for spectrum analysis is obtained by calculation after overlappingspecMaxHoldComprises the following steps:
forming eight paths of parallel signals by the data of two adjacent beats in the current frame of the parallel signals, and respectively writing each beat of the eight paths of parallel signals into a corresponding preset FIFO;
if all the FIFOs are not empty, fourier transform is respectively carried out on the data in each FIFO to obtain a frequency spectrum corresponding to each FIFO, the frequency spectrum is subjected to modulo calculation and maximum preservation to obtain a frequency spectrum for spectrum analysisspecMaxHold。
Further, before selecting the idle tracking channel as the target channel, the method further includes a step of setting the idle tracking channel, which specifically includes: and if the channel parameters of the current tracking channel are not adjacent to or overlapped with the frequency domain information corresponding to the continuous K frames of the current frame containing the parallel signals in the frequency domain, setting the current tracking channel as an idle tracking channel.
Further, the step S3) of the target channel downconverting the current frame of the parallel signals to N parallel signals with different center frequencies according to the channel parameters specifically includes: the target channel is based on carrier frequency information in channel parametersfreConverting the current frame of the parallel signal to zero frequency to obtain a signalsgnMixDown(ii) a Then according to the bandwidth information in the channel parameterbandSelecting corresponding filtering bandwidthfilBandAnd will filter the bandwidthfilBandEqually dividing into N segments to obtain N sub-bandwidthssubFilBandAccording to the sub-bandwidth of each band respectivelysubFilBandCenter frequency of the signalsgnMixDownAnd carrying out down-conversion to obtain N parallel signals with different central frequencies.
Further, the center frequency of the sub-bandwidth is positioned atfre±n·subFilBandAccording to the sub-bandwidth of each band respectivelysubFilBandCenter frequency of the signalsgnMixDownThe step of performing down conversion comprises:
if n =0, according to the frequencyfreGenerating a local oscillator signal of carrier frequency-fsgnMixDownDown conversion f to obtain center frequencyfreThe parallel signal of (a);
if n > 0, according to frequencyn·subFilBandGenerating a local oscillator signal with a carrier frequency of-ndf, and converting the local oscillator signal into a local oscillator signalsgnMixDownDown-converting ndf to obtain a center frequency located atfre-n·subFilBandAnd storing the coefficients used; using stored coefficients for performing the conversion of the signalsgnMixDownConjugate calculation of down-conversion ndf to obtain the signalsgnMixDownThe center frequency of the up-conversion ndf is locatedfre+n·subFilBandOf the parallel signal of (1).
Further, the step of performing a sliding average filtering on the N parallel signals by the target filter in step S3) includes:
acquiring a current parallel signal, and calculating to obtain new data to be added and past data to be subtracted according to the length of a sliding window;
adding new data to the current parallel signal and subtracting the past data, and performing recursive accumulation on each path of signal according to the length of a filter window;
and recombining and adding the results of the recursive accumulation of each path of signals respectively to obtain the result of the current parallel signal sliding mean filtering.
Further, the expression for each path of signal recursively accumulated according to the filter window length is as follows:
y(n + m) = x(6n + m) + x(6(n + 1) + m) + …+ x(6(n + k - 1) + m)
in the above equation, x () is a signal,
() In order to recursively accumulate the results of the summation,nrepresents the nth point in the signal, m is more than or equal to 0 and respectively represents each path in the parallel signal,
is the filter window length.
Further, the expression of recombining and adding the result of recursive accumulation of each path of signals is as follows:
Y(0) = y(n)z -1 + y(n + 1)z -1 + …+ y(n + m)z -1
Y(1) = y(n) + y(n + 1)z -1 + …+ y(n + m)z -1
……
Y(m) = y(n) + y(n + 1) + y(n + 2) + …+ y(n + m)z -1
in the above formula, the first and second carbon atoms are,y(n)…y(n + m) Respectively representing the result of recursive accumulation of each path of the parallel signals,z -1 representing the preset time delay, m is more than or equal to 0, respectively representing each path in the parallel signals,Y(0) …Y(m) Respectively representing the result of the sliding mean filtering of each path in the parallel signals.
Further, the expression of performing up-conversion reduction on the target channel in step S3) is as follows:
in the above formula, the first and second carbon atoms are,frepresents the center frequency in the frequency domain information of the current frame of the parallel signal,lorepresents a filtering bandwidth selected according to a signal bandwidth in the frequency domain information of the current frame of the parallel signal,Lrepresenting a sliding mean filter window length determined by the filter bandwidth,smooth(~,L) Indicating a sliding mean filtering operation with a window length L,sgn filtered representing a filtered parallel signal frame.
The invention also provides a digital channelized system of adaptive tracking filtering, which comprises:
a frequency spectrum detection module for obtaining parallel signals, storing one path of the current frame of the parallel signals, and calculating after overlapping the other path to obtain a frequency spectrum for spectrum analysisspecMaxHoldAcquiring the frequency spectrumspecMaxHoldFrequency domain information of (a);
a tracking channel, configured to serve as a target channel when the frequency domain information is matched or when the frequency domain information is not matched but idle, update a channel parameter of the target channel using the frequency domain information, input a current frame of the parallel signal into the target channel, update a filter parameter of a target filter in the target channel according to the channel parameter, where the target channel includes two filters, and alternately selects one of the two filters as the target filter; then the target channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, and the target filter performs sliding mean filtering on the N parallel signals and performs up-conversion reduction on the target channel to obtain the parallel signal frame filtered by the target filter; and finally splicing the parallel signal frame filtered by the target filter with the parallel signal frame filtered by the previous target filter of the target channel.
Compared with the prior art, the invention has the advantages that:
the invention carries on the spectrum analysis to the signal frame by frame, extracts every signal carrier frequency, bandwidth, forms the correspondent channel parameter, match with the channel parameter association of every tracking channel, if match, upgrade the channel parameter of the corresponding tracking channel, the new signal not matched, assign the idle tracking channel to use; and the tracking channel performs guided filtering channelization processing on the signals according to the channel parameters updated frame by frame, and when the number of frames of the tracking channel, which are not associated with the signals in the spectrum analysis, reaches a preset value K, the state is set to be idle and new signals are waited. Different from general guided filtering, the low-pass filtering adopts a reduction method after mean filtering of frequency division points, and the two groups of parallel filters are adopted for filtering in order to ensure the continuity of signal phases when the same signal spans frames and channel parameters are updated, and then the two groups of filtered signals are spliced and output. Therefore, the invention has the following advantages compared with the general digital channelization:
1. during spectrum analysis, data overlapping is realized through cache control, and the missing detection probability of small pulse width signals is reduced. For small pulse width signals, when the small pulse width signals are divided for 512-point spectrum analysis, the signal intensity is reduced, so that the detection is missed due to the fact that the threshold cannot be exceeded, and the situation that the small signals are divided is avoided due to data overlapping;
2. the channel is not fixed and can be adaptively updated frame by frame for the tracking signal. The channel parameters of the tracking channel are determined by the frequency domain parameters of the actual single-frame signal (and the phase continuity of the filtered cross-frame signal is ensured), so that the channelization result is more accurate and the signal-to-noise ratio is higher;
3. two groups of filters with the same structure are used in the tracking channel, parameters of the two filters are updated alternately, the two filters are controlled to work alternately, and filtering results are spliced. The problem that the phase of a common filtering result is discontinuous at the frame boundary and extra characteristic quantity is introduced is solved;
4. each group of filters is realized by adopting a multipoint mean filtering reduction mode. When the FPGA carries out multi-path parallel signal processing, generally, the use of parallel filtering on DSP resources increases exponentially with the number of parallel paths, the average filtering needs adder resources, the design is reasonable, the adders can be shared by the average filtering with different bandwidths, and the use of FPGA resources is greatly reduced.
5. The signal is no longer divided by channelization. After the signal is subjected to band-pass filtering, the signal does not need to be judged, reconstructed and restored, and the complexity of the system is reduced.
Drawings
Fig. 1 is a schematic diagram of the operation of the embodiment of the present invention.
FIG. 2 is a schematic flow chart of an embodiment of the present invention.
Fig. 3 is a schematic diagram of the operation of the tracking channel according to the embodiment of the present invention.
FIG. 4 is a diagram illustrating data overlapping according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of spectrum calculation according to an embodiment of the present invention.
FIG. 6 is a timing diagram of a filter in the tracking channel according to an embodiment of the invention.
Fig. 7 is a schematic diagram of the alternate operation of the filter according to the embodiment of the present invention.
Fig. 8 is a graph comparing the frequency spectrums of the original signal and the down-converted signal according to the embodiment of the present invention.
Fig. 9 is a schematic diagram of the operation of the filter for down-conversion according to the embodiment of the present invention.
Fig. 10 is a first part of a schematic diagram of the operation of the filter for moving average filtering according to the embodiment of the present invention.
Fig. 11 is a second portion of the schematic diagram of the operation of the filter for moving average filtering according to the embodiment of the present invention.
FIG. 12 is a timing diagram illustrating the outputs of two filters and their splicing results according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
Aiming at the problems existing in the receiving process of non-cooperative signal receiving by the digital channelization technology, analyzing frequency domain information of each signal frame by frame, correlating and matching the frequency domain information with channel parameters of a tracking channel to realize signal tracking, and then selecting the channel parameters by the tracking channel in a self-adaptive manner according to the frequency domain information of the signals, so that the accuracy and the rationality of channelization of the non-cooperative signals are improved, and various problems caused by channel crossing are avoided; the subsequent tracking channel adopts A, B two groups of filters for filtering, so that the continuity of the signal phase after framing filtering is ensured, and no additional signal characteristic quantity is introduced; the two groups of filters are realized by using multi-frequency smooth filtering reduction, generally, in an FPGA circuit running in high-speed multi-path running water, multi-path parallel filtering is realized, DSP resources required by a fir filter are in an exponential relation with the number of paths, an adder is required by smooth filtering, and a smooth mean value can multiplex the adder and a delay line under selectable multi-bandwidth, so that the scheme can greatly reduce the requirement of an algorithm on FPGA chip resources.
Based on the above concept, the present embodiment first performs the following configuration on the FPGA circuit:
the FPGA has a processing clock of 200MHz and inputs 6 parallel zero intermediate frequency complex signalssgn_6x200The instantaneous working bandwidth of the system is 1GHz, the range is +/-500 MHz, 100MHz margins are reserved at the head and the tail respectively, and 4 tracking channels are arranged
Then, a digital channelization method of adaptive tracking filtering is proposed, as shown in fig. 1 and fig. 2, including the following steps:
s1) acquiring a parallel signal, storing one path of a current frame of the parallel signal, and calculating after overlapping the other path to obtain a frequency spectrum for spectrum analysisspecMaxHoldAcquiring a frequency spectrumspecMaxHoldFrequency domain information of (a);
s2) if a tracking channel matched with the frequency domain information exists, taking the tracking channel as a target channel, if the tracking channel matched with the frequency domain information does not exist, selecting an idle tracking channel as the target channel, updating channel parameters of the target channel by using the frequency domain information, inputting a current frame of a parallel signal into the target channel, and updating filtering parameters of a target filter in the target channel according to the channel parameters, wherein in the embodiment, as shown in FIG. 3, each tracking channel is provided with two identical filters, namely a filter A and a filter B, and one of the two identical filters is alternately selected as the target filter after the channel parameters of the target channel are updated each time;
s3) the target channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, then the target filter performs sliding mean filtering on the N parallel signals, and the target channel performs up-conversion reduction to obtain the parallel signal frame filtered by the target filter;
and S4) splicing the parallel signal frame filtered by the target filter with the parallel signal frame filtered by the previous target filter of the target channel, and returning to the step S1) until the parallel signal is completely acquired.
In step S1) of this embodiment, the spectrum for spectrum analysis is obtained by performing overlapping calculationspecMaxHoldThe process of (1) is to perform cache control on six parallel input signals sgn _6x200, and specifically comprises the following two steps:
firstly, forming eight paths of parallel signals by two adjacent beats of data in a current frame of the parallel signals, and respectively writing each beat of the eight paths of parallel signals into a corresponding preset FIFO;
specifically, as shown in fig. 4, each beat of the six-path parallel signal is merged with the remaining data of the previous beatsgn_8x200I.e. eight parallel signals, the first beat in the figure has only 6 data paths, which cannot be made into a complete 8 path,sgn_8x200the back three beats can complete 8 paths when the circuit is invalid, and the front beat has no residual data when the circuit is in the fifth beat, so the circuit can not complete eight paths,sgn_8x200inactive, thus eight-way parallel signalssgn_8x200Cycling in turn every 4 beats;
correspondingly, in the present embodiment, eight parallel signals per beat are read and written by using 8-way FIFO, and 128 data are overlapped in two-way FIFO 8-way FIFO, that is, 16 beatssgn_8x200Each path of independent output 512 points, each path of FIFO data input issgn_ 8x200So that 64 beats of each FIFO input are stopped and there is a case where 3 FIFOs are written simultaneously, each FIFO write signal is controlled by a counter which counts from 0 to 383 cycles whensgn_8x200When active, the counter is incremented by 1 and the control for each FIFO write is detailed in the table below.
TABLE 1 8 FIFO WRITING SIGNAL CONTROL TABLE
Then, when the 8 th FIFO of the 8 ways of FIFOs is not empty, that is, all FIFOs are not empty, as shown in fig. 5, starting to read data from each way of FIFO, calling an FFT IP core of the FPGA to perform 512-point fourier transform on the data read from each FIFO, obtaining 8 ways of frequency spectrums corresponding to each FIFO, performing modulo on the 8 ways of frequency spectrums and performing maximum preservation, wherein the maximum preservation result is a single-frame 512 frequency spectrum corresponding to the single-frame 512 frequency spectrumsgn_6x200512 by 6 point data, i.e. frequency spectrum for spectral analysisspecMaxHoldThe modulo calculation of the frequency spectrum and the maximum preservation are common methods in the field, and the scheme does not involve the improvement of the specific calculation process, and is not described herein again.
In step S1) of this embodiment, a spectrum is acquiredspecMaxHoldPerforming threshold-crossing detection and extraction on the frequency domain information through spectral analysisFrequency spectrumspecMaxHoldThe threshold-crossing detection is a commonly used method in the field, and the scheme does not involve the improvement of the specific calculation process, and is not described herein again.
In step S2) of this embodiment, matching the tracking channel of the frequency domain information means that, if the channel parameter of the tracking channel is adjacent to or overlapped with the frequency domain information in the frequency domain, it is determined that the matching is successful, and therefore, before selecting the idle tracking channel as the target channel, the method further includes a step of setting the idle tracking channel, which specifically includes: and if the channel parameters of the current tracking channel are not adjacent to or overlapped with the frequency domain information corresponding to the continuous K frames of the current frame containing the parallel signals in the frequency domain, setting the current tracking channel as an idle tracking channel. The value of K in this embodiment is 2, i.e. the channel and signal are trackedsgn_6x200When two continuous frames are not matched, the tracking channel is set as an idle tracking channel.
In step S2) of this embodiment, after the current frame of the parallel signal is input to the target channel, in order to implement the alternate operation of the filter a and the filter B, as shown in fig. 7, the configuration pulse is directly sent to the register and the counter corresponding to the filter a, and is inverted and sent to the register and the counter corresponding to the filter B, so that when the value of the configuration pulse is 1, the register and the counter corresponding to the filter a are enabled, and the register and the counter corresponding to the filter B receive a configuration pulse having a value of 0, and therefore, the configuration pulse does not operate, and similarly, when the value of the configuration pulse is 0, the register and the counter corresponding to the filter a do not operate, and the register and the counter corresponding to the filter B are enabled. Acquiring channel parameters of a tracking channel when a register is enabledbpf_coeUpdating the filter parameters of filter A or filter B, for example, selecting filter bandwidth according to bandwidth information in channel parameters, determining the length of the sliding mean filter window according to the filter bandwidth, when the counter is enabled, counting from 0 after receiving configuration pulse until the count value is greater than or equal to the length of the sliding mean filter window, and sending each count value to the corresponding selector, when the count value is less than the length of the sliding mean filter window, the selector outputs high level as the corresponding filterThe input valid flag of filter a or filter B. In this embodiment, the initial value of the configuration pulse is 0, and after the channel parameter is updated, the pulse is generatedbpf_setConfiguring each received pulse as channel parameter update flagbpf_setThe inverse is taken, so that whenever the target channel is configured with the channel parameters, it alternately selects filter a or filter B as the target filter, and updates the filter parameters of the target filter and inputs the valid flag. As shown in fig. 6, whereinbpf_coeIn order to be a parameter of the channel,bpf_setthe flag is updated for the channel parameters and,A_coe、A_validrespectively the filtering parameters and the input valid flag of the filter A, and the same principleB_coe、B_validRespectively, the filter parameters of filter B and the input valid flag, it can be seen that the input valid flags of filter a and filter B alternate with each other, and that each time a pulse occursbpf_setWhen the channel parameter is updated, the filter parameters of the filter A and the filter B are updated to the channel parameter at the momentbpf_coeAnd the input valid flag of the filter A and the filter B is also in the presence of a pulsebpf_setThe opposite is taken out. In this embodiment, the length of the sliding-mean filter window is adjusted so that the input valid flags of filter a and filter B are as long as 512+128 beats, and thus the two input valid flags overlap by 128 beats (6 +128 points), where the overlap is to avoid phase discontinuity of the signal at the frame boundary caused by switching the filter parameters.
In step S3) of this embodiment, the step of down-converting, by the target channel, the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameter specifically includes:
the target channel is based on the channel parametersbpf_coeCarrier frequency information infreParallel signals are transmittedsgn_6x200Obtaining signals by converting the current frame frequency to zero frequencysgnMixDownThe method of converting the signal into zero frequency is a common method in the field, and the scheme does not relate to the improvement of the specific calculation process, and is not described herein again;
then according to the channel parametersbpf_coeMedium bandwidth informationbandSelecting corresponding filtering bandwidthfilBandThe selectable filter bandwidths are 500, 250, 125, 62.5, 31.25, 15.625, 7.8125MHzCorresponding to 6*2, 6*4, 6*8, …, 6 × 64, 6 × 128 points respectivelyAnd (3) mean filtering, wherein the two relations are as follows:
in the above formula, the first and second carbon atoms are,fsfor channel parametersbpf_coeBandwidth of (1), N is the required filtering bandwidthfilBandThe number of equal shares.
In this embodiment, N is 5, so the filtering bandwidth is adjustedfilBandAre equally divided into 5 segments to obtain 5 sub-bandwidthssubFilBandAccording to the sub-bandwidth of each band respectivelysubFilBandCenter frequency of the signalsgnMixDownPerforming down conversion to obtain original center frequencies respectively located atfre，fre±1subFilBand，fre±2subFilBandAnd the 5 parallel signals are all 6 parallel complex signals, as shown in fig. 8, wherein a represents the parallel signalsgn_ 6x200B denotes that the down-converted center frequency is locatedfreC denotes the center frequency after down-conversion is located atfre-1subFilBandD denotes that the down-converted center frequency is located atfre-2subFilBandOf parallel signal spectra.
In this embodiment, the center frequency is locatedfre±n·subFilBand(n is more than or equal to 1) two parallel signals are conjugate and a group of local oscillator mixing frequencies is set to exist in one signalsgnIt is shown below, whereinsgnThe representation of the signal is shown as,arepresenting the real part of the signal,brepresenting the imaginary part of the signal, then:
sgn=a+1j*b（2）
at the same time, set local oscillator signalloIs shown below, whereincRepresenting the real part of it,drepresenting its imaginary part, then:
lo=c+1j*d（3）
will signalsgnUp-conversionloFrequency and down conversion ofloRespectively expressed assgn f lo+ 、sgn f lo- Then, the formula is as follows:
sgn f lo + =sgn×lo=（ac-bd）+1j*（bc+ad）（4）
sgn f lo - =sgn×conj(lo)=（ac+bd）+1j*（bc-ad）（5）
the signals found by the formulae (4) and (5)sgnFor the same local oscillatorloAs a result of the up-conversion and down-conversion,ac,bd,bc,adthe four groups of coefficients are only distinguished by operators and can be multiplexed, thereby reducing DSP resources required by mixing.
Therefore, as shown in fig. 9, in the present embodiment, the sub bandwidths are individually set for each sub bandwidthsubFilBandCenter frequency of the signalsgnMixDownThe step of performing down conversion comprises:
if n =0, depending on the frequencyfreGenerating local oscillator signal with carrier frequency of-f, and multiplying the signal by complex multiplication IP core of FPGAsgnMixDownDown conversion f to obtain center frequencyfreThe parallel signal of (a);
if n > 0, e.g. n is 1 or 2, depending on the frequencyn·subFilBandGenerating a local oscillator signal with a carrier frequency of-ndf, and converting the local oscillator signal into a local oscillator signalsgnMixDownDown-converting ndf to obtain a center frequency located atfre-n·subFilBandAnd storing the coefficients used; using stored coefficients for performing the conversion of the signalsgnMixDownConjugate calculation of down-conversion ndf to obtain the center frequencyfre+n·subFilBandI.e. signals in parallelsgnMixDownThe result of upconverting ndf.
In step S3) of this embodiment, the step of performing sliding mean filtering on the N parallel signals by the target filter includes:
acquiring a current parallel signal, and calculating to obtain new data to be added and past data to be subtracted according to the length of a sliding window;
adding new data to the current parallel signal and subtracting the past data, and performing recursive accumulation on each path of signal according to the length of a filter window;
and recombining and adding the results of the recursive accumulation of each path of signals respectively to obtain the result of the current parallel signal sliding mean filtering.
In this embodiment, the sliding mean filtering is performed on the 6 paths of parallel complex signals after the 5 frequency conversions, and the filtering bandwidth issubFilBandAnd the calculation is completed in a recursive mode, and the expression of the sliding mean filtering is as follows:
wherein the content of the first and second substances,nthe length of the sliding window is long,xin order to be a signal, the signal,yin order to be the result of the filtering,x(2n-1) represents the new data that should be added,x(n-1) represents the past data that should be subtracted. As shown in fig. 10, in this embodiment, a plurality of delay modules of the FPGA are cascaded to form a cascaded delay line, an output of each delay module is connected to the selector, and is selected by the filter selection window, so that the delay module in the above formula can be correspondingly taken outx(2n-1) withx(n-1) for subsequent recursive computation.
As shown in fig. 10 and fig. 11, in the recursive computation, for the current 6 parallel complex signals, after adding the new data and subtracting the past data, the expression for each path of signals is recursively accumulated according to the filter window length as follows:
y(n + m) = x(6n + m) + x(6(n + 1) + m) + …+ x(6(n + k - 1) + m)（7）
in the above equation, x () is a signal,
() In order to recursively accumulate the result of the accumulation,nrepresents the nth point in the signal, m is more than or equal to 0, respectively represents each path in the parallel signal,
for the filter window length, for 6 parallel complex signals, m takes the value of 5, then there are:
the first path is as follows:y(n) = x(6n) + x(6(n + 1)) + …+ x(6(n + k - 1))（8）
and a second path:y(n + 1) = x(6n + 1) + x(6(n + 1) + 1) + …+ x(6(n + k - 1) + 1)（9）
in this recursion, the result of the sixth recursive accumulation is:
y(n + 5) = x(6n + 5) + x(6(n + 1) + 5) + …+ x(6(n + k - 1) + 5)（10）
as shown in fig. 11, the expression for recombining and adding the results of recursive accumulation of each path signal is as follows:
the first path is as follows:Y(0) = y(n)z -1 + y(n + 1)z -1 + …+ y(n + m)z -1 （11）
and a second path:Y(1) = y(n) + y(n + 1)z -1 + …+ y(n + m)z -1 （12）
and recursion is carried out in this way, and the last path is as follows:
Y(m) = y(n) + y(n + 1) + y(n + 2) + …+ y(n + m)z -1 （13）
in the above formula, the first and second carbon atoms are,y(n)…y(n + m) Respectively representing the result of recursive accumulation of each path of the parallel signals,z -1 represents the preset time delay, m is more than or equal to 0, respectively represents each path of the parallel signals, m is 5 in the embodiment,Y(0) …Y(m) Respectively representing the result of the sliding mean filtering of each path in the parallel signals.
In step S3) of this embodiment, when performing upconversion reduction on a target channel, the method performs upconversion reduction on each parallel signal subjected to sliding mean filtering by using a corresponding local oscillator, and then adds all the reduced signals, where an expression is as follows:
in the above formula, the first and second carbon atoms are,frepresents the center frequency in the frequency domain information of the current frame of the parallel signal,lorepresenting a filtering bandwidth selected according to a signal bandwidth in the frequency domain information of the current frame of parallel signals, for a sub-bandwidth corresponding to each of the N parallel signalssubFilBand，LRepresenting a sliding mean filter window length determined by the filter bandwidth,smooth(~,L) Meaning that the mean filtering operation is performed with a window length L,sgn filtered representing the filtered parallel signal frame, so far the target filter in the target channel has finished filtering the current frame of the parallel signal.
In step S4) of the present embodiment, as shown in FIG. 12, the drawingsgn_bandFiled_ARepresents the output of filter A, which has a corresponding output valid signal ofo_valid_A，sgn_bandFiled_BRepresents the output of filter B, whose corresponding output valid signal iso_valid_BThe result after splicing the two groups of filters issgn_bandFiledWith a valid signal ofo_valid. Effective marks of input signals of the two groups of filters are extended by 6 points 128 points in front and at the back on the basis of the frame length 6 points 512, so that the continuity of phases at frame switching positions after the signals are subjected to low-pass filtering and filtering results of the two groups of filters are spliced is ensured. And for the next frame of the parallel signal, if the frequency domain information is obtained according to the step S1), and then the same tracking channel is continuously matched, the tracking channel is used as a target channel, another filter is selected as a target filter, and the processes from the step S2) to the step S4) are executed.
The invention also provides a digital channelized system of the adaptive tracking filtering, which comprises the following components:
a frequency spectrum detection module for obtaining parallel signals, storing one path of the current frame of the parallel signals, and calculating after overlapping the other path to obtain a frequency spectrum for spectrum analysisspecMaxHoldAcquiring a frequency spectrumspecMaxHoldFrequency domain information of (a);
the tracking channel is used for serving as a target channel when the tracking channel is matched with frequency domain information or is not matched with the frequency domain information but is idle, updating channel parameters of the target channel by using the frequency domain information, inputting a current frame of the parallel signals into the target channel, updating filtering parameters of a target filter in the target channel according to the channel parameters, wherein the target channel comprises two filters, and one of the two filters is alternately selected as the target filter; then the target channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, and the target filter performs sliding mean filtering on the N parallel signals and performs up-conversion reduction on the target channel to obtain the parallel signal frame filtered by the target filter; and finally splicing the parallel signal frame filtered by the target filter with the parallel signal frame filtered by the previous target filter of the target channel.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (10)
1. A digital channelization method for adaptive tracking filtering, comprising the steps of:
s1) acquiring a parallel signal, storing one path of a current frame of the parallel signal, and calculating after overlapping the other path to obtain a frequency spectrum for spectrum analysisspecMaxHoldAcquiring the frequency spectrumspecMaxHoldFrequency domain information of (a);
s2) if a tracking channel matched with the frequency domain information exists, taking the tracking channel as a target channel, if the tracking channel matched with the frequency domain information does not exist, selecting an idle tracking channel as the target channel, updating channel parameters of the target channel by using the frequency domain information, inputting a current frame of a parallel signal into the target channel, updating filtering parameters of a target filter in the target channel according to the channel parameters, wherein the target channel comprises two filters, and one of the two filters is alternately selected as the target filter;
s3) the target channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, then the target filter performs sliding mean filtering on the N parallel signals, and the target channel performs up-conversion reduction to obtain the parallel signal frame filtered by the target filter;
and S4) splicing the parallel signal frame filtered by the target filter with the parallel signal frame filtered by the previous target filter of the target channel, and returning to the step S1) until the parallel signal is obtained.
2. The digital channelization method for adaptive tracking filtering according to claim 1, wherein the parallel signals are six parallel signals, and the overlapped parallel signals are calculated to obtain a spectrum for spectrum analysisspecMaxHoldComprises the following steps:
forming eight paths of parallel signals by the data of two adjacent beats in the current frame of the parallel signals, and respectively writing each beat of the eight paths of parallel signals into a corresponding preset FIFO;
if all FIFOs are not empty, fourier transform is respectively carried out on the data in each FIFO to obtain a frequency spectrum corresponding to each FIFO, all frequency spectrums are subjected to modulo operation and maximum preservation to obtain a frequency spectrum for spectrum analysisspecMaxHold。
3. The digital channelization method for adaptive tracking filtering according to claim 1, wherein before selecting an idle tracking channel as a target channel, the method further comprises a step of setting the idle tracking channel, and specifically comprises: and if the channel parameters of the current tracking channel are not adjacent to or overlapped with the frequency domain information corresponding to the continuous K frames of the current frame containing the parallel signals in the frequency domain, setting the current tracking channel as an idle tracking channel.
4. The adaptive tracking filtered digital channelization method of claim 1, wherein the target channel in step S3) down-converts a current frame of parallel signals to different center frequencies according to channel parametersThe N parallel signals specifically include: the target channel is based on carrier frequency information in channel parametersfreConverting the current frame of the parallel signal to zero frequency to obtain a signalsgnMixDown(ii) a Then according to the bandwidth information in the channel parameterbandSelecting corresponding filtering bandwidthfilBandAnd will filter the bandwidthfilBandEqually dividing into N segments to obtain N sub-bandwidthssubFilBandAccording to the sub-bandwidth of each band respectivelysubFilBandCenter frequency of the signalsgnMixDownAnd carrying out down-conversion to obtain N parallel signals with different central frequencies.
5. The adaptive tracking filtered digital channelization method of claim 4, wherein the center frequency of the sub-bandwidth is located atfre±n·subFilBandAccording to respective sub-bandwidthssubFilBandCenter frequency of the signalsgnMixDownThe step of performing down conversion comprises:
if n =0, depending on the frequencyfreGenerating a local oscillator signal of carrier frequency-fsgnMixDownDown conversion f to obtain center frequencyfreThe parallel signal of (a);
if n > 0, according to frequencyn·subFilBandGenerating a local oscillator signal with a carrier frequency of-ndf, and converting the local oscillator signal into a local oscillator signalsgnMixDownDown-converting ndf to obtain a center frequency located atfre-n·subFilBandAnd storing the coefficients used; using stored coefficients for performing the conversion of the signalsgnMixDownConjugate calculation of down-conversion ndf to obtain the signalsgnMixDownThe center frequency of the up-conversion ndf is locatedfre+n·subFilBandOf the parallel signal of (1).
6. The digital channelization method of adaptive tracking filtering according to claim 1, wherein the step of performing sliding mean filtering on the N parallel signals by using the target filter in step S3) comprises:
acquiring a current parallel signal, and calculating to obtain new data to be added and past data to be subtracted according to the length of a sliding window;
adding new data to the current parallel signal and subtracting the past data, and performing recursive accumulation on each path of signal according to the length of a filter window;
and recombining and adding the results of the recursive accumulation of each path of signals respectively to obtain the result of the current parallel signal sliding mean filtering.
7. The method of digital channelization with adaptive tracking filtering of claim 6, wherein the expression for recursive accumulation of each path of signal according to the filter window length is as follows:
y(n + m) = x(6n + m) + x(6(n + 1) + m) + …+ x(6(n + k - 1) + m)
in the above equation, x () is a signal,
() In order to recursively accumulate the results of the summation,nrepresents the nth point in the signal, m is more than or equal to 0, respectively represents each path in the parallel signal,
is the filter window length.
8. The method of claim 7 wherein the recursive accumulation of each signal is re-summed by the following expression:
Y(0) = y(n)z -1 + y(n + 1)z -1 + …+ y(n + m)z -1
Y(1) = y(n) + y(n + 1)z -1 + …+ y(n + m)z -1
……
Y(m) = y(n) + y(n + 1) + y(n + 2) + …+ y(n + m)z -1
in the above formula, the first and second carbon atoms are,y(n)…y(n + m) Respectively representing the result of recursive accumulation of each path of the parallel signals,z -1 representing the preset time delay, m is more than or equal to 0, respectively representing each path in the parallel signals,Y(0) …Y(m) Respectively representing the result of the sliding mean filtering of each path in the parallel signals.
9. The digital channelization method for adaptive tracking filtering according to claim 1, wherein the expression for performing the up-conversion restoration by the target channel in step S3) is as follows:
in the above formula, the first and second carbon atoms are,frepresents the center frequency in the frequency domain information of the current frame of the parallel signal,lorepresents a filtering bandwidth selected according to a signal bandwidth in the frequency domain information of the current frame of the parallel signal,Lrepresenting a sliding mean filter window length determined by the filter bandwidth,smooth(~,L) Indicating a sliding mean filtering operation with a window length L,sgn filtered representing a filtered parallel signal frame.
10. An adaptive tracking filtered digital channelization system, comprising:
a frequency spectrum detection module for obtaining parallel signals, storing one path of the current frame of the parallel signals, and calculating after overlapping the other path to obtain a frequency spectrum for spectrum analysisspecMaxHoldAcquiring the frequency spectrumspecMaxHoldFrequency domain information of (a);
a tracking channel, configured to serve as a target channel when the frequency domain information is matched or when the frequency domain information is not matched but idle, update a channel parameter of the target channel using the frequency domain information, input a current frame of the parallel signal into the target channel, update a filter parameter of a target filter in the target channel according to the channel parameter, where the target channel includes two filters, and alternately selects one of the two filters as the target filter; then the target channel down-converts the current frame of the parallel signals into N parallel signals with different center frequencies according to the channel parameters, and the target filter performs sliding mean filtering on the N parallel signals and performs up-conversion reduction on the target channel to obtain the parallel signal frame filtered by the target filter; and finally splicing the parallel signal frame filtered by the target filter with the parallel signal frame filtered by the previous target filter of the target channel.
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