CN114374407A - Spatial channel characteristic prediction method and system based on m sequence and storage medium - Google Patents
Spatial channel characteristic prediction method and system based on m sequence and storage medium Download PDFInfo
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Abstract
The invention discloses a spatial channel characteristic prediction method, a spatial channel characteristic prediction system and a storable medium based on m sequences, which belong to the technical field of underwater acoustic channel communication and comprise the following steps: setting array parameters and transmitting signal parameters; intercepting a sampling signal according to the set array parameter and the set transmitting signal parameter; carrying out time delay summation beam forming on the intercepted sampling signals; capturing data formed by the delay summation wave beams to obtain a capturing result; generating a delay angle distribution diagram according to the capture result, and predicting the signal delay and the incident angle; and performing Doppler analysis on the path signal according to the signal delay and the incident angle prediction result to complete spatial channel characteristic prediction. The invention solves the problems of traditional direction of arrival prediction, time delay and Doppler, thereby improving the prediction performance of the spatial channel characteristics.
Description
Technical Field
The invention relates to the technical field of underwater acoustic channel communication, in particular to a spatial channel characteristic prediction method and system based on m sequences and a storage medium.
Background
Regardless of the communication mode, the propagation characteristics of the underwater acoustic channel have a significant impact on the effectiveness and reliability of the underwater acoustic communication system. Prior to designing a communication system, sufficient knowledge of the characteristics of the underwater acoustic channel is required, and therefore the underwater acoustic channel is an important aspect in the study of underwater acoustic communications.
Signals may travel many different paths from a transmitter to a receiver. In the process of transmitting underwater acoustic signals, due to the nonuniformity of media, acoustic lines can be bent, and multipath effects are generated through the reflection and refraction of the sea bottom and the sea surface. In a higher level, the energy of the multipath in the actual environment often reaches the receiver in a 'group' manner, so that the multipath which arrives at the same time can be defined as a 'cluster', the occurrence of the multipath cluster is originated from the observation of people in wireless channel measurement, the phenomenon also exists in an underwater channel, the application of the multipath cluster concept can well improve the performance of channel modeling, more channel information is obtained on the basis, and a more excellent communication system is developed. Multipath exists in clusters in a real channel, mainly because of the discrete distribution of scatterers in the physical channel, each of which forms one or several paths through which acoustic energy is transmitted from a transmitter to a receiver. At the receiver, the clusters arrive at different times and from different angles, but the multipath signals within each cluster are very similar. Therefore, clusters can be observed in the delay domain, the angle domain.
In order to better study the characteristics of spatial channels, observe multipath clusters and create a priori knowledge for communication system development, in the prior art, the incident direction of signals is usually predicted by using a traditional direction of arrival prediction (DOA) algorithm, and then signals in different spaces are separated by using beam forming for utilization, without involving the prediction of delay and doppler, or performing delay and doppler prediction on a single receiving channel. Generally speaking, a great number of methods are available for signal direction spectrum prediction, time delay and doppler prediction, but they are used separately, and although the signal direction can be predicted first and then the time delay can be predicted, the problem of resolution is caused, that is, signals with similar arrival directions but different arrival times cannot be resolved, or signals with similar arrival times but different arrival directions cannot be resolved.
Specifically, the problem with the conventional direction of arrival prediction (DOA) method is the following:
the first problem, the problem of coherent source DOA prediction, is that due to the complex propagation environment, especially for communication, signals reach the receiver via reflection and refraction, signals of each path are coherent signal sources, and for coherent signal sources, general DOA prediction algorithms, such as conventional subspace-like algorithms like MUSIC, ESPRIT, etc., cannot effectively distinguish DOAs of signals. The method is generally used for dimension reduction processing, and effective array aperture is sacrificed to realize decoherence, such as a spatial smoothing technology and the like, in the face of some devices with a small number of array elements, complete decoherence on the devices is difficult to achieve, in a communication system, channel characteristics may be complex, the number of coherent signal sources is further increased along with the increase of the number of paths, the channel characteristics are unknown, the number of coherent signal sources reaching a receiver is unknown, and if complete decoherence is needed, great redundancy needs to be left, which causes the array to be very large, and great inconvenience is caused in practical use of the large array.
The second problem, the problem of signal source number prediction, and most algorithms in array signal processing all need to know the number of incident signals, and when the signal source is unknown, the number of the signal source needs to be predicted first or redundancy is left, and when the signal source number prediction has an error, the DOA prediction result will be seriously influenced. The problems need to be further solved for a subspace DOA prediction algorithm, but the classical delay-sum beam forming has the advantage in this aspect, the delay-sum beam forming only needs to change the delay of each array input, scan the incident signal in a certain direction, and then use a matched filter, namely, the input signal is correlated with the sample signal, to obtain the output signal with the maximum signal-to-noise ratio, which is irrelevant to the number of signal incident coherent sources, although the resolution of the delay-sum beam forming is poor and may not be resolved, as long as there is an incident signal in a certain direction, there is a gain in this direction. The delay-sum beam forming method has the advantages of small operand, good robustness and the like, but the delay-sum beam forming method has limited array gain and weak inhibition capability on strong interference, the spatial resolution of the delay-sum beam forming method is limited by Rayleigh limit, and the delay-sum beam forming method has no function on multiple targets in the beam width.
In the category of underwater acoustic communication, if long-distance communication is required, the signal-to-noise ratio reaching a receiving end is low due to large propagation loss in seawater, and interference is serious due to the influence of various severe environments in the sea, which are both required to be solved by a subspace-like DOA algorithm or delay-sum beam forming.
Therefore, how to provide a spatial channel characteristic prediction method, system and storage medium based on m sequences is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
In view of this, the present invention provides a spatial channel characteristic prediction method, system and storage medium based on m-sequence, which solves the problems of traditional DOA prediction, delay and doppler.
In order to achieve the above purpose, the invention provides the following technical scheme:
in one aspect, the present invention provides a spatial channel characteristic prediction method based on m sequences, which comprises the following specific steps:
s100: setting array parameters and transmitting signal parameters;
s200: intercepting a sampling signal according to the set array parameter and the set transmitting signal parameter;
s300: carrying out time delay summation beam forming on the sampling signals;
s400: capturing the data obtained in the step S300 to obtain a capturing result;
s500: generating a delay angle distribution diagram according to the capture result, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
s600: and performing Doppler analysis on the path signals according to the m sequence and the beam forming to complete space channel characteristic prediction.
Preferably, in S100, the setting the array parameter includes:
s110: setting the distance between the information source and the reference array element as follows:
r>>2D2/λ
wherein r is the distance between the information source and the reference array element, D is the array aperture, and lambda is the signal wavelength of the information source;
s120: the array element interval is set as follows:
wherein, d array element spacing;
s130: the number of the array elements is set to be 5-20.
Preferably, in S100, the setting parameters of the transmission signal includes: the transmitting signal is a signal obtained by spreading a single-frequency signal by an m sequence, and the formula is as follows:
C(t)＝c(t)cos(ωct)-c(t)sin(ωct)
wherein c (t) is a spread spectrum signal,
cie (-1,1) is the ith code element of m sequence, TcIs a symbol interval, NmIs the period of the m-sequence,
is a symbol pulse shaping filter, omegacIs the carrier frequency.
Preferably, the S200 intercepting the sampling signal includes: collecting a plurality of array element signals in a transmitting signal, and intercepting a signal with the length of N from each array element signal, wherein N is NmTcIs the length of the spread signal c (t).
Preferably, the S300 performing delay-sum beamforming on the truncated sampling signal includes:
s310: FIR filtering is carried out on the sampling signal intercepted by the mth path (M is 1,2, …, M), and then the output u after the mth path signal filteringm(t)。
S320: for u is pairedm(t) is appropriately carried outTime delays, e.g., τ (m, θ) (corresponding to the amount of delay required for the beam steering angle θ), and summing to obtain beamformed data for steering angle θ
Where theta is within the angular range of the scan
Tau is a period of the m sequence, and all angles in the range are scanned in sequence to obtain data formed by wave beams in each direction;
preferably, the capturing the data obtained in S300 in S400 includes:
s410: detecting the data after the wave beam formation one by one, and judging whether the maximum value in the capturing exceeds a preset threshold value or not;
s420: if the maximum value does not exceed the preset threshold value, returning to S200, comparing the initial point of the intercepted signal with the last time delay of N/2, and intercepting the signal with the length of N from the initial point;
s430: and if the maximum value exceeds a preset threshold value, the acquisition is successful.
Preferably, the S500 includes: combining the successfully captured data formed by the delay summation wave beam with the m sequence to obtain a delay angle distribution diagram, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram.
Preferably, the S600 includes: and according to the data acquired in the step S500, extracting a signal of the path by utilizing the acquired data formed by the successfully acquired delay summation beam and the autocorrelation function of the m sequence to perform Doppler analysis, and completing the spatial channel characteristic prediction.
In another aspect, the present invention provides a spatial channel characteristic prediction system based on m sequences, including:
the preset module is used for setting array parameters and transmitting signal parameters;
the intercepting module is connected with the preset module and is used for intercepting the sampling signal according to the set array parameter and the set transmitting signal parameter;
the processing module is connected with the intercepted signal module and is used for carrying out delay summation beam forming on the intercepted sampling signals;
the capturing module is connected with the processing module and used for capturing the data obtained in the step S300 to obtain a capturing result;
the generating module is connected with the capturing module and used for generating a delay angle distribution diagram according to a capturing result and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
and the prediction module is connected with the generation module and used for performing Doppler analysis on the path signals according to the m sequence and the beam forming to complete space channel characteristic prediction.
In one aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the steps of the m-sequence based spatial channel characteristic prediction method according to any one of claims 1 to 8 when executed by a processor.
As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a spatial channel characteristic prediction method, system and storage medium based on m-sequences, which solves the problems of the conventional DOA prediction, delay and doppler, and improves the spatial channel characteristic prediction performance, specifically:
(1) by combining delay-sum beam forming and rapid parallel capturing, the signal-to-noise ratio of an input signal can be improved, the capturing performance is better than that of a single array element, and more accurate delay and Doppler frequency offset estimation can be obtained;
(2) the time delay-angle distribution diagram can be obtained through the method, the characteristics of the spatial channel can be better analyzed, and a foundation is laid for the development of a communication system;
(3) compared with the common delay summation beam forming, the method has the advantages that the excellent characteristics are inherited, the structure is simple, the estimation of a coherent signal source is not influenced, the problem of resolution is solved, the spatial spectrum is raised to a delay-angle spectrum, the resolution is higher and is not influenced by the number of coherent sources, other strong interference is inhibited through the pseudo-random characteristic of an m sequence, the interference is not influenced by interference signals, and only the signal incidence direction, the delay and the Doppler of a transmitting terminal are estimated;
(4) by the excellent correlation of the m sequence, the signal incidence direction, the time delay and the Doppler can be stably estimated under low signal-to-noise ratio;
(5) the invention does not need to estimate the number of signal sources and the coherent source generated by multipath has no influence on the signal sources, can maintain the aperture of the array unchanged in different channel scenes and has more adaptability;
(6) in a special environment, when a single-frequency signal is not suitable for communication in order to conceal the communication, the m sequence is taken as a typical spread spectrum sequence, and the method is more suitable for a spread spectrum communication system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of a spatial channel characteristic prediction method based on m sequences according to the present invention;
FIG. 2 is a diagram illustrating a fast parallel acquisition algorithm according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a proportional peak decision provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a time-frequency two-dimensional search diagram provided in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a delay-angle distribution according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a spatial channel characteristic prediction system based on m sequences according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
On one hand, referring to fig. 1, the embodiment of the present invention discloses a spatial channel characteristic prediction method based on m-sequences, the spatial channel characteristics include delay, incidence angle and doppler analysis, and the specific steps are as follows:
s100: setting array parameters and transmission signal parameters of spatial channels;
s200: intercepting a sampling signal according to the set array parameter and the set transmitting signal parameter;
s300: carrying out time delay summation beam forming on the sampling signals;
s400: capturing the data obtained in the step S300 to obtain a capturing result;
s500: generating a delay angle distribution diagram according to the capture result, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
s600: and performing Doppler analysis on the path signals according to the m sequence and the beam forming to complete space channel characteristic prediction.
Specifically, in this embodiment, the spreading sequence selects the m-sequence to perform time-frequency two-dimensional search, and because the m-sequence has a better autocorrelation characteristic, when performing sliding correlation, a higher amplitude gain can be obtained by aligning the local m-sequence with the m-sequence in the input signal, and the arrival time of each path can be known.
More specifically, within one period of the m-sequence, i.e., 0. ltoreq. tau. ltoreq.NTcWherein N is the length of the m sequence, TcFor a symbol time width, the m-sequence is a rectangular wave signal with amplitudes of +1 and-1, and the autocorrelation function of the m-sequence is then expressed as:
in a specific embodiment, in order to meet the condition of a signal source in a far field, the array is set to be a one-dimensional, two-dimensional or high-dimensional uniform linear array, and further, uniform linear array parameters and transmission signal parameters need to be set.
Specifically, setting the uniform linear array parameters includes:
for the far field of the desired sound source in the receiving array, where the curvature change of the wave front generated at different array elements when the sound wave reaches the receiving array is negligible, the sound wave propagated as a spherical wave can be reasonably assumed as a plane wave, and the definition of the source in the far field (Fraunhofer) region of the array is:
r>>2D2/λ (2)
wherein r is the distance between the information source and the reference array element, D is the array aperture, and lambda is the signal wavelength of the information source.
Specifically, the present embodiment adopts a uniform linear array, and in order to avoid spatial leakage caused by directional ambiguity, a grating lobe needs not to appear in the range of an incident angle [ -90 °, 90 ° ], and at this time, the array element interval d needs to satisfy:
specifically, for the above narrowband signal sources, the communication signal is generally a wideband signal source, and at this time, the wavelength λ corresponding to each frequency of the signal is different, and at this time, in theory, λ in the formula should be the minimum wavelength λminHowever, the DOA prediction is carried out by using the m sequence, the signal after the m sequence spreads the single-frequency signal can be used as a broadband signal, and the lambda can be the wavelength lambda of the central frequency of the communication signalfcAt least half of the frequency will not generate grating lobes, so the signal coming through the grating lobes will be seriously deformed, the correlation of the m-sequence in the modulation signal will be greatly faded, so the m-sequence used as a matched filter for receiving will not generate a large peak value, and the influence of the propagation characteristic of the high-frequency signal is also generated, so the requirement of the array element spacing d can be relaxed.
More specifically, the number of array elements is also set in this embodiment, and a method of combining delay-sum beam forming with an m-sequence is adopted, so that the more array elements are, the larger the beam forming gain is. In fact, due to the existence of the signal spatial coherence radius, the correlation between the array element receiving signals under the large array aperture condition is weakened, at this time, the array gain cannot be linearly increased along with the increase of the number of the array elements, the number of the array elements is not too small, too small a beam width is caused to be too wide, the DOA prediction can generate angle ambiguity, the number of the array elements needs to be reasonably determined according to the actual use condition, and the number of the array elements is selected to be 10 in the embodiment.
In one embodiment, setting the parameters of the transmitted signal comprises: the transmitting signal is a signal obtained by spreading a single-frequency signal by an m sequence, and the formula is as follows:
C(t)＝c(t)cos(ωct)-c(t)sin(ωct) (4)
wherein c (t) is a spread spectrum signal,
cie (-1,1) is the ith code element of m sequence, TcIs a symbol interval, NmIs the period of the m-sequence,
the present embodiment is a symbol pulse shaping filter, which takes a root-raised cosine filter, and the roll-off coefficient is α, so that the signal bandwidth B is 1+ α)/TcCarrier frequency ωcAnd the array element spacing needs to be reasonably selected according to the requirement of the array element spacing.
In one particular embodiment, truncating the sampled signal comprises: collecting a plurality of array element signals in a transmitting signal, and intercepting a signal with the length of N from each array element signal, wherein N is NmTcIs the length of the spread spectrum signal c (t);
in one embodiment, the delay-and-sum beamforming of the truncated sampled signal comprises:
FIR filtering the sample signal intercepted by the mth path (M is 1,2, …, M), thenThe filtered output of the mth path of signal is um(t)。
For u is pairedm(t) appropriate time delays are made and then summed to obtain beamformed data for a steering angle θ:
where theta belongs to the angular range of the scan [ theta ]min,θmax]Tau is a period of the m sequence, and all angles in the range are scanned in sequence to obtain data formed by wave beams in each direction;
specifically, the specified scanning range is-90 degrees, the delay is carried out on each array element by utilizing the acoustic path difference between the array elements, the delay between the array elements is controlled, the scanning angle can be controlled, and the aim of rotating the directivity of the array wave beam is fulfilled.
More specifically, before the delay, the signal passes through an FIR filter, and a band-pass filter is selected to filter out-of-band interference and improve the signal-to-interference-and-noise ratio of the signal. Delaying each direction of-90 degrees, and finally summing to obtain data formed by wave beams in each direction of-90 degrees;
in a particular embodiment, capturing beamformed data comprises:
since the transmitted signal has only one segment and the receiving end does not know whether the signal comes or not, this embodiment has a different part from the conventional DOA method, and adds a capturing stage.
Specifically, referring to the schematic diagram of the fast parallel acquisition algorithm shown in fig. 2, the data after beam forming is used as input signals to be detected one by one, and then the maximum value is selected and output;
more specifically, referring to fig. 3, it is a schematic diagram of proportional peak determination, and the output result is sent to proportional peak determination to see whether the threshold is exceeded, if the threshold is not exceeded, the process returns, the input data is stepped by N/2 length, and steps S200, S300 and S400 are repeated until the threshold is exceeded, and the capturing is successful.
More specifically, the detailed principle of fast parallel acquisition is:
because of the possible influence of the doppler effect, the signal of the transmitting terminal will be compressed or expanded, resulting in the carrier frequency shift of the transmitted signal, so the local m sequence is multiplied by the cosine signal and the sine signal respectively, to obtain two paths of signals, the formula is as follows,
CI(t)＝c(t)cos(ω0t) (5)
CQ(t)＝c(t)sin(ω0t) (6)
wherein, ω is0Is the local carrier frequency.
As the theory of circular correlation is used, a formula of circular correlation is given, and it is assumed that a cross-correlation expression of a signal x (m) to be processed and a local matching signal h (m) is as follows:
the output of the cross-correlation y (n) fourier transform:
Y(k)＝H(k)X*(k) (8)
therefore, the cross correlation between x (m) and h (m) can be obtained by converting x (m) and h (m) into frequency domains, conjugating one of the frequency domains, multiplying the frequency domains, and performing inverse Fourier transform to obtain a correlation value.
Assuming that the channel is an additive white gaussian noise channel, the input signal r (t) of a single channel can be expressed as (note that r (t) is not data after beamforming, a single channel is derived first, and then data after delay summation is derived):
r(t)＝A(c(t-τ)cos(ω'c(t-τ))-c(t-τ)sin(ω'c(t-τ)))+n(t) (9)
wherein A represents the amplitude of the signal, τ represents the time delay of the signal, ω'cRepresenting the carrier frequency of the received signal, N (t) being additive white Gaussian noise and a bilateral power spectral density of N0/2. Will be provided withr (T) is expressed as r (n) in discrete signal and the sampling interval is Ts。
r(n)＝A(c(n-τn)cos(ω′c(n-τn))-c(n-τn)sin(ω′c(n-τn)))+N(n) (10)
At this time ω'cAnd ω0Representing the digital angular frequency of the received signal, CI(t) is also represented in discrete form:
CI(n)＝c(n)cos(ω0n) (11)
that CI(n) Fourier transform with r (n) to obtain CI(k) And R (k), reacting CI(k) Taking conjugate, multiplying with R (k), and Fourier transforming to obtain CI(n) cyclic correlation with r (n), in the time domain:
wherein N' (N) is CI(n) correlation value with noise, and θ is carrier phase of received signal, since ω'c+ω0Is a high frequency component, and the summed value is smaller than the previous one, so neglecting its influence, and dividing it into noise, can be simplified as follows:
in the same way, C can also be obtainedQ(n) is related to the cycle of r (n), and the expression is as follows:
the carrier frequency is not changed when the one-dimensional search is carried out, and the maximum value of the correlator output is found from all phases of the spread spectrum code, obviously when tau is usednThe first two terms of equations (13) and (14) take the maximum value, and the variable that finally enters the decider is the maximum value in the one-dimensional search.
Let τ benAssuming that the spreading sequence multiplies it by 1, the noise term is ignored, while the euler equation e is usedjxEquation (13) and (14) may be summed up into the complex field:
by converting equations (15) and (16) back to real numbers, one can obtain:
final decision variable YsComprises the following steps:
equation (19) does not contain the carrier phase parameter θ, the size of the decision variable is determined by the carrier frequency offset Δ ω and the spreading sequence length, and if the carrier frequency offset Δ ω is 0 and N is fixed, the decision variable Y is finally determinedsThe maximum value will be taken.
By setting the search frequency of the local carrier according to equation (19), the maximum value can be searched through.
Referring to fig. 4, for the time-frequency two-dimensional search graph provided in this embodiment, the specific frequency domain search step length is set as:
the maximum relative radial velocity v of both communication partiesmax(m/s), the speed of sound c (m/s), and the center frequency of the transmitter f0(Hz), the minimum frequency f of the searchminComprises the following steps:
fmin＝f0(1-vmax/c) (20)
a spreading chip time width of tchipThe spreading sequence has a period of (2)n-1), the frequency minimum resolution Δ f of the spread spectrum signal is then:
Δf＝1/[tchip×(2n-1)] (21)
the step length of frequency search can be set to be Δ f, and it can be seen that the longer the period of the spreading sequence, the more sensitive to doppler shift, and the smaller the search step length. Let the number of channels of the frequency coarse search be NfAnd then:
Nf＝2×(f0-fmin)/Δf (22)
in one embodiment, the successfully captured delay-summed beamformed data is combined with the m-sequence to obtain a delay angle profile, and signal delay and angle of incidence are predicted.
Specifically, the local signal C is obtained by using the data after delay-sum beam forming and the equations (5) and (6)I(n) and CQ(n) performing cyclic correlation, wherein the input signals y (n) are:
wherein r (n-i tau)θ) The data of a single channel after delay compensation is carried out by utilizing the acoustic path difference between array elements, wherein the first array element is taken as a reference array element, if a signal is incident from 0 degree, the compensation is carried out according to an angle theta, so that the delay deviation tau of a second array element signal compared with the first array element is causedθThe Mth array element has a delay offset (M-1) tau from the first array elementθ。
Substituting the beamformed data y (n) for r (n) in equation (12), and dividing the high frequency components into noise to obtain an equation similar to equation (13):
if the starting point of the reference array element input signal and the carrier frequency of the received signal can be obtained by time-frequency two-dimensional search, the tau in the above formula can be usednAnd the frequency deviation delta omega is eliminated to obtain a simplified formula as follows:
equation (25) is also transformed into a complex number using the euler equation:
the above equation is similar to conventional beamforming except that the m-sequence correlation function R (τ) is added, and the array output equation for conventional beamforming is given below for comparison:
comparing equations (26) and (27), it can be seen that R (n-i τ) with a similar weight is added after combining delay-sum beamforming with m-sequenceθ) Firstly, due to the pseudo-randomness of the m sequence, other interference signals are not related to the m sequence, and only the signal of a transmitting terminal is predicted without being influenced by strong interference signals; secondly, the high gain of the correlation function R (tau) can also make the signal forecast under the condition of very low signal-to-noise ratio; meanwhile, when the predicted angle has deviation, the deviation is caused by R (n-i tau)θ) Will attenuate more rapidly than conventional beamforming and therefore be more stable than conventional beamforming. Also, since the arrival times of different paths are usually different, the incident signal can find a unique time corresponding to the angle, which is also the key to observing the multipath clusters.
More specifically, referring to fig. 5, in the delay-angle distribution diagram provided in this embodiment, according to the above principle, the process of jointly predicting the delay and the incident angle is to perform delay-sum beamforming on each direction after eliminating carrier frequency offset by using a time-frequency two-dimensional search predicted carrier frequency, and perform fast parallel capture on the beamformed data to obtain the delay-angle distribution diagram.
In one embodiment, a delay-angle distribution diagram is observed, each cluster of peaks is a cluster of signals of different paths, a signal of one path can be selected, and because the arrival angle and the delay can be seen from the diagram, the signal of the path can be extracted by utilizing the correlation of beam forming and m sequences for Doppler analysis.
On the other hand, referring to fig. 6, the embodiment further provides a spatial channel characteristic prediction system based on m sequences, including:
the preset module is used for setting array parameters and transmitting signal parameters;
the intercepting module is connected with the preset module and is used for intercepting the sampling signal according to the set array parameter and the set transmitting signal parameter;
the processing module is connected with the signal intercepting module and is used for carrying out delay summation beam forming on the intercepted sampling signals;
the capturing module is connected with the processing module and used for capturing the data obtained in the step S300 to obtain a capturing result;
the generating module is connected with the capturing module and used for generating a delay angle distribution diagram according to a capturing result and acquiring the path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
and the prediction module is connected with the generation module and used for performing Doppler analysis on the path signals according to the m sequence and the beam forming to complete space channel characteristic prediction.
In still another aspect, the present embodiment further provides a non-transitory computer-readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the above-mentioned m-sequence-based spatial channel characteristic prediction method.
As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a spatial channel characteristic prediction method, system and storage medium based on m-sequences, which solves the problems of the conventional DOA prediction, delay and doppler, and improves the spatial channel characteristic prediction performance, specifically:
(1) by combining delay-sum beam forming and rapid parallel capturing, the signal-to-noise ratio of an input signal can be improved, the capturing performance is better than that of a single array element, and more accurate delay and Doppler frequency offset estimation can be obtained;
(2) the time delay-angle distribution diagram can be obtained through the method, the characteristics of the spatial channel can be better analyzed, and a foundation is laid for the development of a communication system;
(3) compared with the common delay summation beam forming, the method has the advantages that the excellent characteristics are inherited, the structure is simple, the estimation of a coherent signal source is not influenced, the problem of resolution is solved, the spatial spectrum is raised to a delay-angle spectrum, the resolution is higher and is not influenced by the number of coherent sources, other strong interference is inhibited through the pseudo-random characteristic of an m sequence, the interference is not influenced by interference signals, and only the signal incidence direction, the delay and the Doppler of a transmitting terminal are estimated;
(4) by the excellent correlation of the m sequence, the signal incidence direction, the time delay and the Doppler can be stably estimated under low signal-to-noise ratio;
(5) the invention does not need to estimate the number of signal sources and the coherent source generated by multipath has no influence on the signal sources, can maintain the aperture of the array unchanged in different channel scenes and has more adaptability;
(6) in a special environment, when a single-frequency signal is not suitable for communication in order to conceal the communication, the m sequence is taken as a typical spread spectrum sequence, and the method is more suitable for a spread spectrum communication system.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The spatial channel characteristic prediction method based on the m sequence is characterized by comprising the following specific steps:
s100: setting array parameters and transmitting signal parameters;
s200: intercepting a sampling signal according to the set array parameter and the set transmitting signal parameter;
s300: carrying out time delay summation beam forming on the sampling signals;
s400: capturing the data obtained in the step S300 to obtain a capturing result;
s500: generating a delay angle distribution diagram according to the capture result, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
s600: and performing Doppler analysis on the path signals according to the m sequence and the beam forming to complete space channel characteristic prediction.
2. The m-sequence-based spatial channel characteristic prediction method according to claim 1, wherein in S100, setting the array parameters comprises:
s110: setting the distance between the information source and the reference array element as follows:
r>>2D2/λ
wherein r is the distance between the information source and the reference array element, D is the array aperture, and lambda is the signal wavelength of the information source;
s120: the array element interval is set as follows:
wherein, d array element spacing;
s130: the number of the array elements is set to be 5-20.
3. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein the setting of the parameters of the transmission signal in S100 comprises: the transmitting signal is a signal obtained by spreading a single-frequency signal by an m sequence, and the formula is as follows:
C(t)＝c(t)cos(ωct)-c(t)sin(ωct)
wherein c (t) is a spread spectrum signal,
cie (-1,1) is the ith code element of m sequence, TcIs a symbol interval, NmIs the period of the m-sequence,
is a symbol pulse shaping filter, omegacIs the carrier frequency.
4. The m-sequence based spatial channel characteristic prediction method of claim 3, wherein the S200 truncating the sampled signal comprises: collecting a plurality of array element signals in a transmitting signal, and intercepting a signal with the length of N from each array element signal, wherein N is NmTcIs the length of the spread signal c (t).
5. The method according to claim 1, wherein the S300 performing delay-sum beamforming on the truncated sampled signals comprises:
s310: FIR filtering is carried out on the sampling signal intercepted by the mth path (M is 1,2, …, M), and the filtered output of the mth path is um(t)；
S320: for u is pairedm(t) time-delaying, and then summing to obtain beamformed data for a steering angle θ:
where theta belongs to the angular range of the scan [ theta ]min,θmax]And tau is a period of the m sequence, and all angles in the range are scanned in sequence to obtain data formed by the wave beams in each direction.
6. The method for predicting spatial channel characteristics based on m-sequences according to claim 4, wherein the specific process of S400 capturing the data obtained in S300 includes:
s410: detecting the data after the wave beam formation one by one, and judging whether the maximum value in the capturing exceeds a preset threshold value or not;
s420: if the maximum value does not exceed the preset threshold value, returning to S200, comparing the initial point of the intercepted signal with the last time delay of N/2, and intercepting the signal with the length of N from the initial point;
s430: and if the maximum value exceeds a preset threshold value, the acquisition is successful.
7. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein the S500 comprises: combining the successfully captured data formed by the delay summation wave beam with the m sequence to obtain a delay angle distribution diagram, and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram.
8. The m-sequence based spatial channel characteristic prediction method according to claim 1, wherein the S600 comprises: and according to the data acquired in the step S500, extracting a signal of the path by utilizing the acquired data formed by the successfully acquired delay summation beam and the autocorrelation function of the m sequence to perform Doppler analysis, and completing the spatial channel characteristic prediction.
9. An m-sequence based spatial channel characteristic prediction system, comprising:
the preset module is used for setting array parameters and transmitting signal parameters;
the intercepting module is connected with the preset module and is used for intercepting the sampling signal according to the set array parameter and the set transmitting signal parameter;
the processing module is connected with the intercepted signal module and is used for carrying out delay summation beam forming on the intercepted sampling signals;
the capturing module is connected with the processing module and used for capturing the data obtained in the step S300 to obtain a capturing result;
the generating module is connected with the capturing module and used for generating a delay angle distribution diagram according to a capturing result and acquiring a path signal and angle and delay information corresponding to the path signal according to the delay angle distribution diagram;
and the prediction module is connected with the generation module and used for performing Doppler analysis on the path signals according to the m sequence and the beam forming to complete space channel characteristic prediction.
10. A non-transitory computer-readable storage medium storing a computer program, wherein the computer program is configured to, when executed by a processor, implement the steps of the m-sequence based spatial channel characteristic prediction method according to any one of claims 1 to 8.
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