CN117856847A - Adaptive beam forming device under complex coherent interference based on space difference - Google Patents
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Abstract
The invention provides a self-adaptive beam forming device under complex coherent interference based on space difference, and relates to the field of array antenna self-adaptive beam forming. The method utilizes a special space differential technology SDT to construct a new differential matrix, utilizes the characteristic that the differential matrix removes independent interference and noise influence and simultaneously recovers correct coherent interference signal subspace, improves the traditional MVDR self-adaptive beam forming device and recovers ideal inhibition performance of coherent interference in incidence. On the basis, the MVDR beam forming device which is ideal to restrain independent interference and the MVDR beam forming device which is improved by the differential matrix which is ideal to restrain coherent interference are combined to obtain the combined processing beam forming device which is simultaneously restrained to the incident mixed interference. The invention has more accurate interference null angle and deeper inhibition depth for the inhibition of complex coherent interference, has better robustness under small block beats and smaller input dry-to-noise ratio, and is suitable for the condition that the incident interference number is larger than the array element number.
Description
Technical Field
The invention provides a self-adaptive beam forming device under complex coherent interference based on space difference, and relates to the field of array antenna self-adaptive beam forming.
Technical Field
Adaptive beamforming is a key research technique in the field of array signal processing. The array antenna calculates the received sampling data by using the technology, and can adaptively adjust the weight parameters of the receiving array elements to obtain a specific incoming expected signal required by main lobe alignment and form a deep null wave beam pattern at the position of high-power interference of side lobes. The adaptive beamforming is widely used in the fields of military radar, remote sensing, wireless communication and the like due to the characteristics of flexibility, instantaneity and the like, and the efficient interference signal suppression performance is realized under the condition of undistorted receiving of a desired signal. However, in practical engineering applications, when there is a non-ideal situation of coherent interference in the facing environment, such as multipath propagation of interference or intelligent interference in military, most adaptive beamformers will not obtain an ideal pattern for forming deep nulls at all interference angles, resulting in a significant reduction or even failure of the interference suppression performance in the airspace. Therefore, how to improve the robustness of an adaptive beamformer in this type of environmental application, so as to achieve suppression of complex interference incidence is a very important research content.
Essentially, the existence of coherent interference causes a rank deficit phenomenon to occur in the covariance matrix of the array received signals, so that the conventional adaptive beamformer cannot obtain a correct interference signal subspace and a correct noise subspace. The general solution idea is to do a certain solution to the existing array covariance matrix to recover the rank of the matrix, and then apply it to improve the traditional adaptive beamformer. The existing decoherence technology can be divided into two main categories according to whether the effective aperture is lost or not, namely a method based on space smoothing and a method based on matrix reconstruction. The principle based on the space smoothing method is to attempt to obtain a covariance matrix after decorrelated by dividing the whole array into a plurality of overlapped subarrays and performing special operation processing, which has the defects of needing to lose the space freedom of the array, so that the side lobe of the directional diagram of the adaptive beam forming device obtained by correction is higher and the total number of processable interference can only be smaller than the number of array elements of the array, but has the advantages of simple processing and small calculation amount. The principle of the matrix reconstruction-based method is that the covariance matrix of reduced rank is rearranged to obtain a Toeplitz matrix, a Hankel matrix or other matrixes of full rank, or an equivalent covariance matrix is integrated and reconstructed under the condition of known interference to priori information, the method has the defects of high calculation complexity and large calculation amount (the former method is used for carrying out feature decomposition to obtain an interference signal subspace and a noise subspace, the latter method is used for carrying out complex integral operation), but the method has the advantages that the array degree of freedom is not required to be lost, so that lower side lobes and deeper interference angle nulls can be obtained, and the processable interference number is increased to a certain extent.
However, the existing improved adaptive beamformer has a disadvantage of suppressing the incident interference, which is mainly characterized in that most of the cases that only the interference incident by the array is fully coherent are considered, and complex coherent interference, that is, the cases that multiple independent interference and multiple groups of coherent interference exist simultaneously, are not considered. It is therefore desirable to obtain an adaptive beamformer with more desirable interference rejection angle accuracy and nulling performance in such complex environments and to achieve as much processing of the incident interference as possible.
Disclosure of Invention
Aiming at the problem of uniform linear array self-adaptive beam forming in complex coherent interference environments with multiple independent and multiple groups of coherent interference, the invention is based on a special spatial differential technology SDT, and the spatial differential matrix reconstructed by the technology has the characteristic of recovering the rank as the coherent interference number, so that the MVDR beam forming device is improved, and the ideal suppression performance of the coherent interference is recovered, namely, the nulling of the directional diagram at all coherent interference angles is recovered, and the angle precision and the suppression depth of the interference nulling are improved. Furthermore, by combining the MVDR beam forming device with the space difference matrix improvement and the original traditional MVDR beam forming device, the combined processing self-adaptive beam forming device capable of processing multiple independent and multiple groups of coherent interference incidence simultaneously is provided by a special structure, ideal null suppression is generated in all interference directions, and the total number of the interference which can be processed under certain conditions is allowed to exceed the limit of the array element number.
The technical scheme adopted for achieving the aim is as follows:
an adaptive beamformer under complex coherent interference based on spatial differentiation, comprising the steps of:
s1: constructing a mixed signal model for acquisition and reception signals at each array element of a uniform linear array
The array received signal contains 3 parts, i.e. the desired signalNumber x s (t) interference signal x i (t) and noise Signal x n (t)，
x(t)＝x s (t)+x i (t)+x n (t)
Assume a total of K+1 far-field narrowband signals at an angle θ k K=0, …, K incident, 1 st signal is the desired signal s 0 (t) the angle of arrival is θ 0 The method comprises the steps of carrying out a first treatment on the surface of the The back K are interference signals, and the incoming angle is theta k K=1, …, K, including the former K u The individual interference coming from different sourcesAnd K of total L coherent groups c Interference, K u Each independent interference is uncorrelated with L coherent interference groups. The relation of the coherent interference parts can be expressed as +.>Wherein l is a coherent group index, K c,l The number of interference in the first coherent group is shown, the interference in each coherent group comes from the same independent signal source, and the signal sources in different coherent groups are uncorrelated, i.e. there are L coherent signal source groups->In addition, it is assumed that the complex correlation coefficient vector of the first group of coherent interference is +.>Therein () T Representing transposed matrices.
Assuming that the uniform linear array contains the number M of array elements and the interval between the array elements is d, the first array element is taken as the reference to the signal wavelength lambda and the angle theta k The corresponding guiding vector is
From the above assumption, the hybrid received signal model of the uniform linear array acquisition can be expressed as,
x(t)＝a(θ 0 )s 0 (t)+A u s u (t)+A c Γs c (t)+x n (t)
wherein A is u And A c An array manifold vector matrix corresponding to independent interference and coherent interference respectively, and Γ is a coherence coefficient matrix.
S2: constructing an array covariance matrix under a mixed signal model
The array covariance matrix may be expressed as
E [. Cndot.]Representing the mathematical expectation of solving () H Representing the conjugate transpose of the matrix. R is R u ＝E[s u (t)s u (t) H ]And R is c ＝E[s c (t)s c (t) H ]The signal covariance matrixes of the incidence of the independent signal sources corresponding to the independent interference and the coherent interference groups respectively,sum sigma 2 Average power of desired signal power and noise incidence, respectively, I M Is an M-dimensional unit array.
S3: calculating a sampling covariance matrix based on an array covariance matrix model under a mixed signal model using a sampling signal sequence x (n) in actual array reception to approximate a mathematical expectation solution in the array covariance matrix
S4: constructing a smooth subarray covariance matrix
Based on the prior maximum number of coherent interference within the coherent groupFirstly, the array is divided into p overlapped subarrays in a space smoothing way (p is more than or equal to p is needed to be satisfied) max Usually take p=p max ) Each subarray contains M-p+1 array elements, so that the array element receiving signals of each subarray can calculate the corresponding subarray coordinationVariance matrix
G in the formula m Is an M-p +1 row selection matrix.
S5: constructing differential matrix by using space differential technology
By using the obtained p space smoothing covariance matrices, a special differential matrix can be constructed, and the differential processing mode can be described as
J herein M-p+1 Is an inverse unit array () * Representing conjugating the matrix. The spatial differential processing results in a matrix D p The desired signal portion, the independent interference portion, and the noise portion have been removed to contain only the portion of coherent interference, and the rank of the matrix is restored to be consistent with the total number of coherent interference.
S6: improved MVDR beam former for effectively suppressing coherent interference
Using a spatial differential matrix D p Replacement of array covariance matrix R in a conventional MVDR beamformer x Improved MVDR beamformer
Of the formula (I)Is to use the corresponding expected pointing angle theta only when the first M-p+1 array elements in the array 0 Is to be determined(s) -1 Representing inverting the matrix. The output of the weight vector after processing the coherent interference in the reception can be expressed as
It can be seen that the processing output from the interference section input is 0, and the ideal suppression performance of the interference has been restored in theory at this time.
S7: solving for a joint processing adaptive beamformer weight vector that can handle multiple independent and multiple sets of coherent interference incidents simultaneously
Wherein the method comprises the steps ofIs a Kronecker product operation.
S8: splicing the array received data to obtain an equivalent received signal vector to be processed
The corresponding joint processing array steering vector can be deduced asThus, in order to combine all the incident interference in the received signal and simultaneously suppress the interference, we can combine the received signal vector x (t) of all M array elements with the received signal of the first M-p+1 array elements in the array->The following is made
S9: suppressing the mixed receiving interference and outputting the result
Using joint processing weight vectorsFor equivalent received signal vector after splicing +.>Performing the treatment, i.e
Therein, whereinIs a conventional MVDR beamformer and considers the case where the desired signal is not included in the reception. The visual output achieves both ideal nulling suppression for the independent interference portion (conventional MVDR beamforming can form ideal deep nulls at independent interference to angles) and ideal nulling suppression for the coherent interference portion.
Drawings
Fig. 1 is a flow chart of acquisition and application of an adaptive beam former under complex coherent interference based on spatial differential;
FIG. 2 is a schematic illustration of spatial smoothing applied in differential matrix construction;
FIG. 3 is a directional diagram comparison of the adaptive beamformer of the present invention with an optimal MVDR beamformer without coherent interference input, a conventional MVDR beamformer, an MVDR beamformer with improved FBSS decorrelation, an MVDR beamformer with improved matrix reconstruction MEVM;
FIG. 4 is a graph comparing the interference null angle root mean square error RMSE of the adaptive beamformer of the present invention with the variation curve of the input interference free interference input optimal MVDR beamformer, conventional MVDR beamformer, FBSS decoherence improved MVDR beamformer, matrix reconstructed MEVM improved MVDR beamformer;
FIG. 5 is a graph comparing the variation of the interference null angle root mean square error RMSE of the adaptive beamformer of the present invention with the variation of the sampling snapshot number N with the optimal MVDR beamformer without coherent interference input, the conventional MVDR beamformer, the MVDR beamformer with improved FBSS decorrelation, the MVDR beamformer with improved matrix reconstruction MEVM;
FIG. 6 is a graph comparing the output SINR of the adaptive beamformer of the present invention with the variation curve of the input SINR INR with the optimal MVDR beamformer without coherent interference input, the conventional MVDR beamformer, the MVDR beamformer with improved FBSS decorrelation, and the MVDR beamformer with improved matrix reconstruction MEVM;
FIG. 7 is a graph comparing the output SINR of the adaptive beamformer of the present invention with the variation of the number of samples N of the optimal MVDR beamformer without coherent interference input, the conventional MVDR beamformer, the MVDR beamformer with improved FBSS decorrelation, and the MVDR beamformer with improved matrix reconstruction MEVM;
fig. 8 is a graph comparison of the adaptive beamformer of the present invention with an optimal MVDR beamformer without coherent interference input, a conventional MVDR beamformer, an FBSS decorrelated improved MVDR beamformer, a matrix-reconstructed MEVM improved MVDR beamformer when the total number of incident interference is greater than the number of array elements.
Detailed description of the preferred embodiments
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
the invention may be embodied in different forms and is not limited to the specific examples described;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and specific embodiments.
Example 1
The embodiment provides a method for constructing and applying an adaptive beam forming device under complex coherent interference based on space difference, which is shown in a flow chart in fig. 1, and comprises the following steps:
s1: inputting continuous N times of sampling signal data received by M array elements of a uniform linear array;
s2: computing a sampling covariance matrix from an array of N samples received signal vectorsMathematical expectation solution R for approximation array covariance matrix x ＝E[x(t)x(t) H ]Where t=n×t, n=1, …, N;
s3: calculating a sampling covariance matrix R of a smooth sub-matrix using the sampling covariance matrix m ,m＝1,…,p；
S4: constructing a special differential matrix D by using a space differential technology p ；
S5: an improved MVDR beamformer is constructed to effectively suppress coherent interference;
s6: calculating a joint processing self-adaptive beam former weight vector capable of processing a plurality of independent and multi-group coherent interference incidence simultaneously, and updating the processor weight of each array element in the array;
s7: splicing the array received data to obtain an equivalent received signal vector to be processed;
s8: equivalent received signal vector to be processed after splicing and reorganizing by using self-adaptive updated array weight vector pairPerforming interference suppression processing;
s9: and (5) ending the interference suppression of the received mixed signal and outputting a processing result.
Example 2
The present embodiment continues to disclose the following on the basis of embodiment 1:
the mixed signal model input by the array in the step S1 specifically comprises the following steps: the array receives a signal comprising 3 parts, i.e. the desired signal x s (t) interference signal x i (t) and noise Signal x n (t)，
x(t)＝x s (t)+x i (t)+x n (t)
Assuming that a total of k+1 far-field narrowband signals are incident at an angle θk, k=0, …, K, 1 st signal is the desired signal s 0 (t) the angle of arrival is θ 0 The method comprises the steps of carrying out a first treatment on the surface of the The back K are interference signals, and the incoming angle is theta k K=1, …, K, including the former K u The individual interference coming from different sourcesAnd K of total L coherent groups c Interference, K u Each independent interference is uncorrelated with L coherent interference groups. The relation of the coherent interference parts can be expressed as +.>Wherein l is a coherent group index, K c,l The number of interference in the first coherent group is shown, the interference in each coherent group comes from the same independent signal source, and the signal sources in different coherent groups are uncorrelated, i.e. there are L coherent signal source groups->In addition, it is assumed that the complex correlation coefficient vector of the first group of coherent interference is +.>Therein () T Representing transposed matrices.
Assuming that the uniform linear array contains the number M of array elements and the interval between the array elements is d, the first array element is taken as the reference to the signal wavelength lambda and the angle theta k The corresponding guiding vector is
From the above assumption, the hybrid received signal model of the uniform linear array acquisition can be expressed as,
x(t)＝a(θ 0 )s 0 (t)+A c s c (t)+A c Гs c (t)+x n (t)
wherein A is u And A c An array manifold vector matrix corresponding to independent interference and coherent interference respectively, and Γ is a coherence coefficient matrix.
The array covariance matrix under the mixed signal model in step S2 is actually expressed as
E [. Cndot.]Representing the mathematical expectation of solving () H Representing the conjugate transpose of the matrix. R is R u ＝E[s u (t)s u (t) H ]And R is c ＝E[s c (t)s c (t) H ]Incidence of independent signal sources corresponding to independent interference and coherent interference groups respectivelyIs a signal covariance matrix of (a),sum sigma 2 Average power of desired signal power and noise incidence, respectively, I M Is an M-dimensional unit array.
The method for constructing the smooth subarray covariance matrix in the step S3 specifically comprises the following steps: first according to the prior maximum interference number in the coherent groupThe array is divided into p overlapped subarrays in a space smoothing way (p is more than or equal to p is required to be satisfied) max Usually take p=p max ) Each subarray contains M-p+1 array elements, so that the array element receiving signals of each subarray can calculate the covariance matrix of the corresponding subarray
G in the formula m Is an M-p +1 row selection matrix.
The method for constructing the differential matrix by using the spatial differential technology in the step S4 specifically comprises the following steps: using the resulting p spatially smoothed covariance matrices, the differential processing approach can be described as
J herein M-p+1 Is an inverse unit array () * Representing conjugating the matrix. The spatial differential processing results in a matrix D p The desired signal portion, the independent interference portion, and the noise portion have been removed to contain only the portion of coherent interference, and the rank of the matrix is restored to be consistent with the total number of coherent interference.
Step 5 using spatial differential matrix D p Replacement of array covariance matrix R in a conventional MVDR beamformer x Improved MVDR beamformer with efficient suppression of coherent interference
Of the formula (I)Is to use the corresponding expected pointing angle theta only when the first M-p+1 array elements in the array 0 Is to be determined(s) -1 Representing inverting the matrix. The output of the weight vector after processing the coherent interference in the reception can be expressed as
It can be seen that the processing output from the interference section input is 0, and the ideal suppression performance of the interference has been restored in theory at this time.
The joint processing adaptive beamformer weight vector obtained in step S6, which simultaneously processes multiple independent and multiple sets of coherent interference incidences, is represented as
Step S7, splicing the array received data to obtain an equivalent received signal vector to be processed, specifically: the received signal vector x (n) of all M array elements of the array is compared with the received signals of the first M-p+1 array elements in the arrayThe following is made
Step S8, the adaptive updated joint processing adaptive beam former weight vector is applied to carry out interference suppression processing on the equivalent received signal vector to be processed, and the processing procedure and the output result are expressed as the following formulas
Therein, whereinIs a conventional MVDR beamformer and considers the case where the desired signal is not included in the reception. The visual output achieves both ideal nulling suppression for the independent interference portion (conventional MVDR beamforming can form ideal deep nulls at independent interference to angles) and ideal nulling suppression for the coherent interference portion.
Example 3
This embodiment provides the following specific examples on the basis of embodiment 1 and embodiment 2:
the simulation parameters set are as follows: the number of array elements M=8, the array element spacing d=0.5 times the minimum incident signal wavelength, and the number of the space smoothing subarrays p=2. The ambient noise is Additive White Gaussian Noise (AWGN). The incident mixed receiving signal contains 1 expected signal, the incident angle is 0 degree, and the power is 5dB; k (K) u =2 independent interference signals, incidence angles are-18 ° and 17 °, respectively; k (K) c =4 coherent interference signals, l=2 coherent groups and each group contains K c,1 ＝K c,2 The incidence angles are [ -32 °,55 °, -58 °,35 ° ] respectively]The amplitude and phase angle of the complex coherence coefficient of the coherence group are [0.9,0.8,0.85,0.7 ]]And [135 °,70 °,98 °,231 °]. In the comparison of beam patterns, we command the incident power of the desired signal to be 5dB, all K u The power of the independent signal source of +L interference is 15dB, the power of the environmental noise is 0dB, the sampling snapshot number is set to be N=500, the scanning angle resolution is 0.1 DEG, and the range is [ -90 DEG, 90 DEG]. The adaptive beam forming device, the traditional MVDR beam forming device and the MVDR beam forming device which are improved by the prior decorrelation technology are applied to process the mixed received signals, and the optimal MVDR beam forming device without coherent interference input is used as a reference to respectively obtain beam patterns, as shown in figure 3. The specific analysis is as follows: the OPTIMAL, MVDR, FBSS-MVDR, MEVM-MVDR and SDT-MVDR patterns in fig. 3 correspond to the best MVDR beamformers without coherent interference input, respectivelyConventional MVDR beamformers, FBSS decoherence improved MVDR beamformers, matrix reconstructed MEVM improved MVDR beamformers, and the spatial differential SDT improved adaptive beamformers of the present invention. It can be seen that the conventional MVDR beamformer suppresses nulling to angle with interference in the presence of coherent interference, whereas both the decorrelated techniques FBSS and MEVM modified MVDR beamformers and the SDT modified MVDR beamformers of the present invention recover more accurate nulling at all interference angles and remain undistorted at the desired pointing angle. It is apparent that the pattern of the beamformer of the present invention has lower side lobes compared to it.
Further keeping the simulation parameters of the array model and the input mixed signal model unchanged by varying the input interference power (all K u +L independent interference signal sources have the same power) to change the input dry noise ratio (INR) between 0dB and 35dB, and under the condition of sampling snapshot number N=500, calculating the Root Mean Square Error (RMSE) of the interference null angle generated by the beam forming device and the compared beam forming device directional diagram and the real interference incoming angle, wherein the Monte Carlo experiment number is 10000, and obtaining the performance comparison of the interference suppression angle precision. The specific analysis is as follows: as shown in fig. 4, as the INR increases, the interference angle nulling performance of the optimal MVDR beamforming for which both the FBSS improved MVDR beamforming and the SDT improved MVDR beamforming of the present invention can be well progressively referenced, and the MEVM improved MVDR beamforming is insensitive to INR variations and has poor performance. Whereas conventional MVDR beamformers have relatively large RMSE errors because they cannot handle the correlation case. In addition, at lower INR, the SDT-improved MVDR beamforming of the present invention has less interference nulling error than the FBSS-improved MVDR beamforming; while at high INR the FBSS improved MVDR beamforming is slightly less estimated than the SDT improved MVDR beamforming of the present invention, the difference is small and converges to the reference optimal MVDR beamforming.
Meanwhile, compared with the suppression effect of each beam former on interference in the output of the processed mixed received signals under different input INRs, the Monte Carlo experiment times are 10000 times, and the signal-to-interference-and-noise ratio (SINR) of the output is calculated. The specific analysis is as follows: as shown in fig. 6, the beamformer of the present invention has the highest output SINR at any input INR, which benefits from high interference angle suppression accuracy and null depth and lower pattern side lobe suppression of noise, and as the input INR increases, the larger the output SINR, because the adaptive beamformer essentially gets interference null depth proportional to the input interference power with maximum output signal-to-interference-plus-noise ratio as an optimization criterion.
The robustness of the interference suppression performance advantage of the adaptive beamformer of the present invention at different sample bursts N was then explored. Changing n=50-500 under the condition of keeping inr=15 dB, comparing the interference suppression angle precision of each beam former, and the number of Monte Carlo experiments is 10000, and the specific analysis is as follows: as shown in fig. 5, it can be seen that both the FBSS-modified MVDR beamforming and the SDT-modified MVDR beamforming of the present invention can well approximate the interference angle nulling accuracy of the optimal MVDR beamforming, regardless of whether the snapshot is small or large, while the error of the MEVM-modified MVDR beamforming is large. Meanwhile, the influence of different snapshot numbers N on the output SINR of interference suppression when each beam former processes the input mixed received signal is also compared, and the specific analysis is as follows: as shown in fig. 7, the beamformer of the present invention has the highest output SINR, either for small or large snapshots, illustrating its robustness to the interference suppression performance advantage in the input signal.
Finally, if the array model and other parameters are kept unchanged, only the number of the input mixed signals is changed, and the situation that the actual interference number K contained in the simulated input mixed signals is larger than the array element number M of the array is simulated, specifically: the system comprises 1 expected signal, the incident angle is 0 DEG, and the power is 5dB; k (K) u The angles of incidence of the 4 independent interference signals are [ -42 °, -28 °,39 °,63 °,]；K c =6 coherent interference signals, l=3 coherent groups and each group contains K c,1 ＝K c,2 ＝K c,3 The angles of incidence are [30 °,51 °, -54 °,17 °, -62 °, -17 ° ], respectively, =2 coherent interference]The amplitude and phase angle of the complex coherence coefficient of the coherence group are [1,0.9,1,0.7,0.85,1 ]]And [122 °,237.2 °,78 °,112.5 °,66 °,126 °]. Application of the inventionThe adaptive beam forming device is used for processing the mixed received signals, and the optimal MVDR beam forming device without coherent interference input is used as a reference to respectively obtain beam patterns so as to compare the interference suppression nulling performance of the beam forming device. As shown in fig. 8. The specific analysis is as follows: it can be seen that in contrast to the conventional MVDR beamformer, FBSS improved MVDR beamformer, MVEM improved MVDR beamformer and the reference optimal MVDR beamformer without coherent interference input, except the SDT improved MVDR beamformer of the present invention, are unable to form a correct nulling at all practical interference angles, since the number of interference they can handle is limited by the number of array elements, whereas the adaptive beamformer of the present invention has a theoretical maximum number of interference that can be handled under the parameters of the present simulation model of k=10>The number of array elements m=8, at which point the ideal nulling suppression at all interference angles can be decorrelated and correctly recovered.
Finally, it should be noted that: the above-described embodiments are only for illustrating the technical aspects of the present invention, not for limiting the same, and although the present invention has been described in detail with reference to the above examples, it should be understood by those of ordinary skill in the art that: various modifications and equivalent arrangements may be made to the specific embodiments or modes of carrying out the invention, and it is not necessary or exhaustive of all embodiments. Any modification, equivalent replacement and improvement which does not depart from the spirit and scope of the present invention should be construed to be included in the scope of the appended claims.
Claims (9)
1. An adaptive beamformer under complex coherent interference based on spatial differencing, characterized in that the construction and application comprises the steps of:
s1: inputting continuous N times of sampling signal data received by M array elements of a uniform linear array;
s2: computing a sampling covariance matrix from an array of N samples received signal vectorsMathematical expectation solution R for approximation array covariance matrix x ＝E[x(t)x(t) H ]Where t=n×t, n=1, …, N;
s3: calculating a sampling covariance matrix R of a smooth sub-matrix using the sampling covariance matrix m ,m＝1,…,p；
S4: constructing a special differential matrix D by using a space differential technology p ；
S5: an improved MVDR beamformer is constructed to effectively suppress coherent interference;
s6: calculating a joint processing self-adaptive beam former weight vector capable of processing a plurality of independent and multi-group coherent interference incidence simultaneously, and updating the processor weight of each array element in the array;
s7: splicing the array received data to obtain an equivalent received signal vector to be processed;
s8: equivalent received signal vector to be processed after splicing and reorganizing by using self-adaptive updated array weight vector pairPerforming interference suppression processing;
s9: and (5) ending the interference suppression of the received mixed signal and outputting a processing result.
2. The method for constructing an adaptive beamformer under spatially-differential-based complex coherent interference according to claim 1, wherein the mixed signal model of the array input in step S1 is specifically: the array receives a signal comprising 3 parts, i.e. the desired signal x s (t) interference signal x i (t) and noise Signal x n (t)，
x(t)＝x s (t)+x i (t)+x n (t)
Assume a total of K+1 far-field narrowband signals at an angle θ k K=0, …, K incident, 1 st signal is the desired signal s 0 (t) the angle of arrival is θ 0 The method comprises the steps of carrying out a first treatment on the surface of the The back K are interference signals, and the incoming angle is theta k K=1, …, K, including the former K u The individual interference coming from different sourcesSignal sourceAnd K of total L coherent groups c Interference, K u Each independent interference is uncorrelated with L coherent interference groups. The relation of the coherent interference parts can be expressed as +.>Wherein l is a coherent group index, K c,l The number of interference in the first coherent group is shown, the interference in each coherent group comes from the same independent signal source, and the signal sources in different coherent groups are uncorrelated, i.e. there are L coherent signal source groups->In addition, it is assumed that the complex correlation coefficient vector of the first group of coherent interference is +.>Therein () T Representing transposed matrices.
Assuming that the uniform linear array contains the number M of array elements and the interval between the array elements is d, the first array element is taken as the reference to the signal wavelength lambda and the angle theta k The corresponding guiding vector is
From the above assumption, the hybrid received signal model of the uniform linear array acquisition can be expressed as,
x(t)＝a(θ 0 )s 0 (t)+A u s u (t)+A c Γs c (t)+x n (t)
wherein A is u And A c An array manifold vector matrix corresponding to independent interference and coherent interference respectively, and Γ is a coherence coefficient matrix.
3. The method for constructing an adaptive beamformer under spatially-differential-based complex coherent interference according to claim 1, wherein the array covariance matrix under the mixed signal model in step S2 is actually expressed as
E [. Cndot.]Representing the mathematical expectation of solving () H Representing the conjugate transpose of the matrix. R is R u ＝E[s u (t)s u (t) H ]And R is c ＝E[s c (t)s c (t) H ]The signal covariance matrixes of the incidence of the independent signal sources corresponding to the independent interference and the coherent interference groups respectively,sum sigma 2 Average power of desired signal power and noise incidence, respectively, I M Is an M-dimensional unit array.
4. The method for constructing an adaptive beamformer under spatially-differential-based complex coherent interference according to claim 1, wherein the method for constructing a smooth subarray covariance matrix in step S3 is specifically as follows: first according to the prior maximum interference number in the coherent group The array is divided into p overlapped subarrays in a space smoothing way (p is more than or equal to p is required to be satisfied) max Usually take p=p max ) Each subarray contains M-p+1 array elements, so that the array element receiving signals of each subarray can calculate the covariance matrix of the corresponding subarray
G in the formula m Is an M-p +1 row selection matrix.
5. The method for constructing an adaptive beamformer under complex coherent interference based on spatial differentiation according to claim 1, wherein the method for constructing a differential matrix by spatial differentiation in step S4 specifically comprises: using the resulting p spatially smoothed covariance matrices, the differential processing approach can be described as
J herein M-p+1 Is an inverse unit array () * Representing conjugating the matrix. The spatial differential processing results in a matrix D p The desired signal portion, the independent interference portion, and the noise portion have been removed to contain only the portion of coherent interference, and the rank of the matrix is restored to be consistent with the total number of coherent interference.
6. The method for constructing adaptive beamformer under complicated coherent interference based on spatial differentiation according to claim 1 wherein step S5 uses spatial differential matrix D p Replacement of array covariance matrix R in a conventional MVDR beamformer x Improved MVDR beamformer with efficient suppression of coherent interference
Of the formula (I)Is to use the corresponding expected pointing angle theta only when the first M-p+1 array elements in the array 0 Is to be determined(s) -1 Representing inverting the matrix. The output of the weight vector after processing the coherent interference in the reception can be expressed as
It can be seen that the processing output from the interference section input is 0, and the ideal suppression performance of the interference has been restored in theory at this time.
7. The method for constructing adaptive beamformer under spatially-differential-based complex coherent interference according to claim 1, wherein the joint processing adaptive beamformer weight vector obtained in step S6 for simultaneously processing multiple independent and multiple groups of coherent interference incidences is expressed as
8. The method for applying the adaptive beam former under the complex coherent interference based on the spatial difference as set forth in claim 1, wherein the step S7 is to splice the array received data to obtain an equivalent received signal vector to be processed, specifically: the received signal vector x (n) of all M array elements of the array is compared with the received signals of the first M-p+1 array elements in the arrayThe following is made
9. The method of applying adaptive beamformer under spatially-differential-based complex coherent interference according to claim 1, wherein step S8 applies adaptively updated joint processing adaptive beamformer weight vectors to perform interference suppression processing on equivalent received signal vectors to be processed, the processing and output results being formulated as
Therein, whereinIs a conventional MVDR beamformer and considers the case where the desired signal is not included in the reception. The visual output achieves both ideal nulling suppression for the independent interference portion (conventional MVDR beamforming can form ideal deep nulls at independent interference to angles) and ideal nulling suppression for the coherent interference portion.
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