An Adaptive Beam Generation Method and System

Abstract

An adaptive beamforming method and system disclosed by the present invention. The method is a new method of eigenprojection based on interference matrix estimation, which solves the problem of the decrease in output signal-to-interference-plus-noise ratio (SINR) caused by the mismatch of the target steering vector in the adaptive beamforming technology. The implementation process is as follows: calculating the sample covariance matrix using the training data collected by the array radar; reconstructing the interference matrix by means of the target blocking matrix and matrix eigenvalue transformation; estimating the target covariance matrix by removing the interference matrix from the sample covariance matrix; obtaining the signal subspace projection operator through eigenvalue decomposition of the target covariance matrix to complete the eigenprojection of the target prior steering vector; and finally calculating the weight vector using the interference matrix and the target steering vector to achieve beamforming. The present invention improves the output performance of the beamformer when both the angle of arrival (AoA) and array calibration errors exist, and can be applicable to strong / weak target scenarios.

Description

Technical Field

[0001] The present invention relates to the field of array signal processing, and particularly to an adaptive beam generation method and system. Background Art

[0002] Adaptive beamforming technology is widely used in aviation, aerospace, radar, and communication systems. By forming a gain in the target direction and a null in the interference direction, the output signal-to-interference-and-noise ratio (SINR) is improved. However, in the actual working environment, there are array element position errors, channel amplitude and phase errors, etc., resulting in deviations in the target steering vector constraint. Theoretical research shows that when there is a constraint deviation in the target steering vector, in the case of low signal-to-noise ratio (SNR), problems such as main lobe deviation may occur, reducing the output SINR; in the case of high SNR, if the received data contains the target, there may even be a signal 'self-cancellation' phenomenon, resulting in a sharp deterioration of the output SINR.

[0003] Regarding the problem of solving the optimal weight for adaptive beamforming under multiple errors, typical solutions include: diagonal loading methods, subspace algorithms, constrained optimization methods, interference covariance matrix reconstruction methods, etc. Among them:

[0004] Diagonal loading methods: This method artificially injects noise and adds a small amount to the diagonal elements of the covariance matrix of the sampled sample data, thereby reducing the perturbation degree of the noise eigenvalues in the sampled covariance matrix and correspondingly reducing the influence of the noise eigenvectors on the weight vector in the beamforming process. The advantage is to improve the robustness of the algorithm to the number of snapshots and slow down the signal'self-cancellation' phenomenon. However, it also has the disadvantages of shallower nulls at the interference positions in the beam pattern, a decrease in the output SINR, and difficulty in controlling the loading value.

[0005] Subspace algorithms: Under the condition that the signal source and noise are independent and incoherent with each other, this method uses the orthogonality between the signal interference subspace and the noise subspace to project the target steering vector onto the signal interference subspace, thereby discarding the component of the weight vector in the noise subspace and weakening the influence of the perturbation of the noise subspace on the performance of the beamforming algorithm. This algorithm has good robustness to the uncertainty of the target steering vector caused by any error, but this algorithm is only applicable to high SNR environments and requires accurate knowledge of the dimension of the signal interference subspace. Otherwise, the performance of the beamformer will decline sharply.

[0006] Constrained optimization methods: These methods utilize convex optimization tools and generally aim to maximize the output power or the output signal-to-interference-plus-noise ratio (SINR). They optimize the steering vector by constraining the target steering vector within the uncertainty set of the prior steering vector or by constraining it to be close to the signal interference subspace. This algorithm can achieve good performance when the steering vector is accurately constrained. However, once there are errors, the ability to constrain the steering vector decreases, and the algorithm ultimately cannot optimize to obtain the optimal solution. Additionally, constrained optimization methods generally have a high computational complexity.

[0007] Interference covariance matrix reconstruction methods: These methods utilize the spatial sparsity of signals and integrate in the regions where interference may occur using, for example, the Capon power spectrum, PI spectrum, and SPICE spectrum to estimate the interference covariance matrix without the target to improve the performance of the beamformer. Then, they use the reconstructed interference covariance matrix and combine some optimization methods to estimate the steering vector, enabling the array to achieve good output performance. However, this method requires accurate array structure information, that is, it only considers the direction-of-arrival error of the signal and does not consider element position errors, amplitude-phase errors, etc., resulting in a mismatch of the interference covariance matrix in the actual working environment.

[0008] The above-mentioned diagonal loading methods can improve the performance under small snapshots, but there is a phenomenon of target "self-cancellation" under strong targets; subspace methods can improve the estimation performance of the steering vector under strong targets, but there are large errors under weak targets; existing constrained optimization methods have limited performance improvement due to deviations in the steering vector constraint; interference covariance matrix reconstruction methods can significantly improve the performance of the adaptive beamformer under accurate array calibration, but lose the interference suppression ability under array calibration errors. Summary of the Invention

[0009] The object of the present invention is to provide an adaptive beam generation method and system, and a new method of feature projection based on interference matrix estimation is proposed to solve the problem of the decrease in output SINR caused by steering vector mismatch.

[0010] In a first aspect, the present invention provides an adaptive beam generation method, including:

[0011] Calculate a sampling covariance matrix based on the collected sampling data, where the sampling data includes one target data and multiple interference data detected by the detection beam emitted by the radar echoer;

[0012] Process the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix;

[0013] Subtract the reconstructed interference matrix from the sampling covariance matrix to obtain an estimated target covariance matrix;

[0014] A beam weight vector is obtained based on the estimated target covariance matrix and the reconstructed interference matrix;

[0015] The radar echo configuration parameters are corrected according to the beam weight vector, so that the radar echo generates a corrected adaptive beam.

[0016] Furthermore, the adaptive beam generation method further includes:

[0017] Combined with a uniform linear array, the sampling data at a set time is collected.

[0018] Furthermore, the blocking matrix is formed according to the radar search configuration parameters according to the low-rank approximation rule.

[0019] Furthermore, the process of obtaining a reconstructed interference matrix by processing the sampling covariance matrix with a blocking matrix includes:

[0020] The sampling covariance matrix is processed with the blocking matrix to obtain a quasi-interference covariance matrix;

[0021] Matrix eigenvector transformation is performed on partial principal eigenvectors of the quasi-interference covariance matrix to obtain the reconstructed interference matrix.

[0022] Furthermore, the process of obtaining a beam weight vector based on the estimated target covariance matrix and the reconstructed interference matrix includes:

[0023] The estimated target covariance matrix is processed to obtain a target steering vector;

[0024] The beam weight vector is calculated based on the target steering vector and the reconstructed interference matrix.

[0025] Furthermore, the process of processing the estimated target covariance matrix to obtain a target steering vector includes

[0026] The estimated target covariance matrix is eigen-decomposed to obtain the eigenvectors of the estimated target covariance matrix;

[0027] The correlation coefficient between the eigenvectors and the radar target prior steering vector is calculated to determine the eigen-projection operator, and then the target steering vector is obtained.

[0028] In a second aspect, the present invention provides an adaptive beam generation system, including:

[0029] Sampling covariance matrix module: calculates a sampling covariance matrix according to the collected sampling data, where the sampling data includes a target data and multiple interference data detected by a detection beam emitted by a radar echo;

[0030] Reconstruction interference matrix module: Process the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix;

[0031] Estimated target covariance matrix module: Subtract the reconstructed interference matrix from the sampling covariance matrix to obtain an estimated target covariance matrix;

[0032] Beam weight vector calculation module: Obtain a beam weight vector based on the estimated target covariance matrix and the reconstructed interference matrix;

[0033] Correction module: Correct the radar echo configuration parameters according to the beam weight vector so that the radar echo generates a corrected adaptive beam.

[0034] Further, the adaptive beam generation system further includes:

[0035] Sampling module: Collect the sampling data at a set time in combination with a uniform linear array.

[0036] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the adaptive beam generation methods are implemented.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. The computer program is characterized in that when the computer program is executed by a processor, the steps of any one of the adaptive beam generation methods are implemented.

[0038] Advantages of the present invention

[0039] The present invention provides an adaptive beam generation method and system. By constructing an angle-expanded blocking matrix using the target prior angle, then removing the target components of the training data with the help of the blocking matrix, and then correcting the target steering vector in combination with the feature projection technology, adaptive beamforming under angle error and array structure mismatch can be achieved. Description of the Drawings

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic flowchart of the adaptive beam generation method in the embodiments of the present invention;

[0042] Figure 2It is a schematic diagram of array receiving data in an embodiment of the present invention;

[0043] Figure 3 is a relationship diagram of the output SINR and the input SNR of two comparison methods in the embodiment of the present invention under different errors;

[0044] Figure 4 is a relationship diagram of the output SINR and the number of snapshots of two comparison methods in the embodiment of the present invention under different errors.

[0045] Figure 5 It is a schematic structural diagram of an electronic device in an embodiment of the present invention. Specific embodiments

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Currently, when there is a constraint deviation in the target steering vector, in the case of low signal-to-noise ratio (SNR), problems such as main lobe deviation may occur, reducing the output SINR; in the case of high SNR, if the received data contains a target, even a signal 'auto-cancellation' phenomenon may occur, resulting in a sharp deterioration of the output SINR.

[0048] Based on this, the present invention provides an adaptive beam generation method, including:

[0049] Calculating a sampling covariance matrix according to the collected sampling data, where the sampling data includes a target data and a plurality of interference data detected by a detection beam emitted by a radar echoer;

[0050] Processing the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix;

[0051] Subtracting the reconstructed interference matrix from the sampling covariance matrix to obtain an estimated target covariance matrix;

[0052] Obtaining a beam weight vector according to the estimated target covariance matrix and the reconstructed interference matrix;

[0053] Modifying the configuration parameters of the radar echoer according to the beam weight vector so that the radar echoer generates a corrected adaptive beam.

[0054] In some other embodiments, the adaptive beam generation method further includes:

[0055] Collect the sampling data at a set time in combination with a uniform linear array.

[0056] In some other embodiments, the blocking matrix is formed by radar search configuration parameters according to the low-rank approximation rule.

[0057] In some other embodiments, the processing of the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix includes:

[0058] Process the sampling covariance matrix with the blocking matrix to obtain a quasi-interference covariance matrix;

[0059] Perform matrix eigenvector transformation on some of the main eigenvectors of the quasi-interference covariance matrix to obtain the reconstructed interference matrix.

[0060] In some other embodiments, the obtaining of a beam weight vector according to the estimated target covariance matrix and the reconstructed interference matrix includes:

[0061] Process the estimated target covariance matrix to obtain a target steering vector;

[0062] Calculate the beam weight vector according to the target steering vector and the reconstructed interference matrix.

[0063] In some other embodiments, the processing of the estimated target covariance matrix to obtain a target steering vector includes

[0064] Perform eigenvalue decomposition on the estimated target covariance matrix to obtain the eigenvectors of the estimated target covariance matrix;

[0065] Calculate the correlation coefficient between the eigenvectors and the radar target prior steering vector to determine the eigenprojection operator, and then obtain the target steering vector.

[0066] On the other hand, the present invention provides an adaptive beamforming system, including:

[0067] Sampling covariance matrix module: Calculate the sampling covariance matrix according to the collected sampling data, where the sampling data includes a target data and multiple interference data detected by a detection beam emitted by a radar echoer;

[0068] Reconstructed interference matrix module: Process the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix;

[0069] Estimated target covariance matrix module: Subtract the reconstructed interference matrix from the sampling covariance matrix to obtain the estimated target covariance matrix;

[0070] Beam weight vector calculation module: Obtain a beam weight vector according to the estimated target covariance matrix and the reconstructed interference matrix;

[0071] Correction module: Correct the radar echo configuration parameters according to the beam weight vector, so that the radar echo generates a corrected adaptive beam.

[0072] In some other embodiments, the adaptive beam generation system further includes:

[0073] Sampling module: Collect the sampling data at a set time in combination with a uniform linear array.

[0074] Adaptive beamforming technology focuses on the undistorted output of the target while ensuring sufficient suppression of interference, which requires accurate reconstruction of the interference covariance matrix and constraint of the target steering vector. See Figure 1 , the adaptive beam generation method includes the following steps:

[0075] Step (1): Calculate the sampling covariance matrix: Use a uniform linear array with M elements arranged at half-wavelength intervals to collect data X(t) and calculate the sampling covariance matrix (The echo information of X(t) contains 1 target and J interferences);

[0076] Step (2): Reconstruct the interference matrix: Use the low-rank approximation technology to construct a target blocking matrix B, and use B to process to obtain the quasi-interference covariance matrix Perform matrix eigenvector transformation on the first J principal eigenvectors of to reconstruct the interference matrix

[0077] Step (3): Estimate the target covariance matrix: Use the definition of the covariance matrix to directly subtract from to estimate the target covariance matrix

[0078] Step (4): Steering vector projection correction: Perform eigenvalue decomposition on , and determine the eigenprojection operator P by calculating the correlation coefficient between the eigenvector of and the target prior steering vector , so as to obtain the corrected target steering vector

[0079] Step (5): Estimate the optimal weight vector: Use and to calculate the optimal weight vector w to complete adaptive beamforming.

[0080] In a specific implementation, the target blocking matrix B is constructed by means of the low-rank approximation technique described in step (2), and B is used to process Get the quasi-interference covariance matrix right The first J main eigenvectors of are transformed into matrix features to reconstruct the interference matrix The specific steps include:

[0081] 2.1(a) Construct the target blocking matrix B with the help of low-rank approximation technology:

[0082]

[0083] S and U represent positive definite matrices respectively The eigenvalue matrix and the corresponding eigenvector matrix composed of the first L large eigenvalues obtained by eigendecomposition. trace(·) represents the matrix trace. (·) H represents the conjugate transpose, a(θ) represents the array steering vector corresponding to the angle θ, I represents the unit matrix, To use Noise power of the minimum eigenvalue estimate;

[0084] 2.1(b) Processing using B Get the quasi-interference covariance matrix

[0085]

[0086] 2.1(c) Yes The first J main eigenvectors of are transformed into matrix features to reconstruct the interference matrix

[0087]

[0088] S J and U J Respectively The eigenvalue matrix and the corresponding eigenvector matrix composed of the first J large eigenvalues obtained by eigendecomposition.

[0089] The pair described in step (4) Perform eigendecomposition and calculate The eigenvector and the target priori guidance vector The correlation coefficient of is used to determine the feature projection operator P, thereby obtaining the corrected target-oriented vector The specific steps include:

[0090] 4.1(a) Yes Perform eigendecomposition:

[0091]

[0092] n i , where \(i = 1, 2, \ldots, M\) represents eigenvalues, which are arranged in descending order as \(n_1 \gt n_2 \gt \ldots \gt n\) M , \(p\) i , where \(i = 1, 2, \ldots, M\) represents the corresponding eigenvectors;

[0093] 4.1(b) By calculating the correlation coefficient between the eigenvector of and the target prior steering vector:

[0094]

[0095] 4.1(c) Determine the eigenprojection operator \(P\):

[0096] Arrange \(Cor(i), i = 1, 2, \ldots, M\) in descending order of correlation as \(Cor\) [M] \(\geq Cor\) [M-1] \(\geq \ldots \geq Cor\) [1] , then the corresponding eigenvectors are \(p\) [M] , \(p\) [M-1] , \(\ldots, p\) [1] , and then determine the number of main eigen - space dimensions \(m\) through the following criterion:

[0097]

[0098] If \(\rho\) is a manually - set correlation threshold, then the eigenprojection operator \(P\) can be obtained as:

[0099] \(P = [p\) [M] , \(p\) [M-1] , \(\ldots, p\) [m] [p\) [M] , \(p\) [M-1] , \(\ldots, p\) [m] H

[0100] 4.1(d) Thus, obtain the modified target steering vector

[0101]

[0102] The present invention will be further described below in conjunction with specific embodiments:

[0103] Embodiment 1:

[0104] Step 1: Calculate the sampling covariance matrix. Refer to Figure 2 , and specifically, it is implemented by using a uniform linear array with \(M\) elements arranged at half - wavelength intervals to collect data \(X(t)\) and calculate the sampling covariance matrix ​

[0105]

[0106] K represents the number of snapshots.

[0107] Step 2: Reconstruct the interference matrix, and the specific implementation is as follows:

[0108] 2(a) Construct the target blocking matrix B by means of low-rank approximation technology:

[0109]

[0110] S and U respectively represent the eigenvalue matrix composed of the first L large eigenvalues obtained by eigenvalue decomposition and the corresponding eigenvector matrix, trace(·) represents matrix trace, (·) H represents conjugate transpose, a(θ) represents the array steering vector corresponding to the angle θ, and I represents the identity matrix. for using the noise power estimated by the minimum eigenvalue;

[0111] 2(b) Process with B to obtain the quasi-interference covariance matrix

[0112]

[0113] 2(c) Perform matrix eigenvalue transformation on the first J principal eigenvectors of

[0114]

[0115] S J and U J respectively represent the eigenvalue matrix composed of the first J large eigenvalues obtained by eigenvalue decomposition and the corresponding eigenvector matrix.

[0116] Step 3: Estimate the target covariance matrix, and the specific implementation is as follows:

[0117] Using the definition of the covariance matrix, directly subtract from to estimate the target covariance matrix

[0118]

[0119] Step 4: Steering vector projection correction, and the specific implementation is as follows:

[0120] 4.1(a) Perform eigenvalue decomposition on :

[0121]

[0122] n i , where \(i = 1, 2, \ldots, M\) represents the eigenvalues, which are arranged in descending order as \(n_1 \gt n_2 \gt \ldots \gt n\) M , \(p\) i , where \(i = 1, 2, \ldots, M\) represents the corresponding eigenvectors;

[0123] 4.1(b) By calculating the correlation coefficient between the eigenvector of and the target prior steering vector

[0124]

[0125] 4.1(c) Determine the eigenprojection operator \(P\):

[0126] Arrange \(Cor(i), i = 1, 2, \ldots, M\) in descending order of correlation as \(Cor\) [M] \(\geq Cor\) [M-1] \(\geq \ldots \geq Cor\) [1] , then the corresponding eigenvectors are \(p\) [M] , \(p\) [M-1] , \(\ldots, p\) [1] , and then determine the number of main eigenvector spaces \(m\) through the following criterion:

[0127]

[0128] If \(\rho\) is a user-defined correlation threshold, then the eigenprojection operator \(P\) can be obtained as:

[0129] \(P = [p\) [M] , \(p\) [M-1] , \(\ldots, p\) [m] [p\) [M] , \(p\) [M-1] , \(\ldots, p\) [m] H , (10)

[0130] 4.1(d) Thus, obtain the modified target steering vector

[0131]

[0132] Step 5: Estimate the optimal weight vector, which is specifically implemented as:

[0133] Use and to calculate the optimal weight vector \(w\):

[0134] ​

[0135] The present invention estimates the interference steering matrix by using a blocking matrix and eigen - transformation, and then modifies the beam - forming weight vector by using an eigen - projection method to achieve adaptive beam - forming.

[0136] Example 2:

[0137] Experimental conditions: Uniform linear array (number of array elements \(M = 10\)), element spacing is \(0.5\lambda\) (\(\lambda=0.05\)), number of signal sources is 3, including one target, the true angle of the target is \(10^{\circ}\) (prior angle is \(8^{\circ}\)), the true angles of two interferences are \(- 25^{\circ}\), \(40^{\circ}\) (prior angles are \(-23^{\circ}\), \(38^{\circ}\)), the interference - to - noise ratio is 40 dB for both; each test is carried out with 200 Monte Carlo simulations;

[0138] Simulation parameters: The target interval of the present invention is set to The integration interval is \(0.1^{\circ}\), and the correlation threshold is 0.6. The existing technologies for comparison are set as follows: For the eigen - subspace projection method, the number of signal sources is accurately known; for the diagonal loading method, the loading parameter is 10.

[0139] Experimental results: When there is only an error in the target arrival direction, the input SNR is increased from - 20 dB to 30 dB, the number of snapshots is fixed at 50 times, and the relationship between the output SINR and the input SNR is shown in Fig. 3(a);

[0140] Observing Fig. 3(a), it is found that when there is only an error in the target arrival direction, the performance advantage of the present invention is significant, and the output SINR can be maximized in the entire input SNR interval.

[0141] Example 3:

[0142] Experimental conditions and simulation parameters are the same as those in Example 2. At the same time, the experimental conditions also include element position errors that follow a uniform distribution in \((-0.05\lambda,0.05\lambda)\);

[0143] Experimental results: When there are both an error in the target arrival direction and element position errors, the input SNR is increased from - 20 dB to 30 dB, the number of snapshots is fixed at 50 times, and the relationship between the output SINR and the input SNR is shown in Fig. 3(b);

[0144] Observing Fig. 3(b), it is found that when there are both an error in the target arrival direction and element position errors, the performance advantage of the present invention is obvious compared with other methods, and an almost ideal output effect can be obtained in the entire input SNR interval.

[0145] Example 4:

[0146] Experimental conditions and simulation parameters are the same as those in Example 2. At the same time, the experimental conditions also include amplitude - phase errors, the amplitude error follows a uniform distribution in \((-5\mathrm{dB},5\mathrm{dB})\), and the phase error follows a uniform distribution in \((-5^{\circ},5^{\circ})\);

[0147] Experimental results show that when there are both target arrival direction errors and amplitude-phase errors, increasing the input SNR from -20 dB to 30 dB with the number of snapshots fixed at 50 times, the relationship between the output SINR and the input SNR is shown in Fig. 3(c).

[0148] Observing Fig. 3(c), it is found that when there are both target arrival direction errors and amplitude-phase errors, the performance of the present invention is close to ideal in the entire input SNR range, while the robustness of other algorithms is average.

[0149] Example 5:

[0150] The experimental conditions and simulation parameters are the same as those in Example 2;

[0151] Experimental results show that when there is only a target arrival direction error, increasing the number of snapshots from 10 times to 100 times with the input SNR fixed at 10 dB, the relationship between the output SINR and the number of snapshots is shown in Fig. 4(a).

[0152] Observing Fig. 4(a), it is found that when there is a target arrival direction error, the method of the present invention requires fewer snapshots, and the performance is basically stable when the number of snapshots is 40 times.

[0153] Example 6:

[0154] The experimental conditions and simulation parameters are the same as those in Example 2, and at the same time, the experimental conditions also include element position errors that follow a uniform distribution of (-0.05λ, 0.05λ);

[0155] Experimental results show that when there are both target arrival direction errors and element position errors, increasing the number of snapshots from 10 times to 100 times with the input SNR fixed at 10 dB, the relationship between the output SINR and the number of snapshots is shown in Fig. 4(b).

[0156] Observing Fig. 4(b), it is found that when there are both target arrival direction errors and element position errors, the robustness of the method of the present invention to the number of snapshots is significantly improved, and it basically converges when the number of snapshots is 40 times.

[0157] Example 7:

[0158] The experimental conditions and simulation parameters are the same as those in Example 2, and at the same time, the experimental conditions also include amplitude-phase errors, where the amplitude error follows a uniform distribution of (-0.5 dB, 0.5 dB) and the phase error follows a uniform distribution of (-5°, 5°);

[0159] Experimental results show that when there are both target arrival direction errors and amplitude-phase errors, increasing the number of snapshots from 10 times to 100 times with the input SNR fixed at 10 dB, the relationship between the output SINR and the number of snapshots is shown in Fig. 4(c).

[0160] Observing Fig. 4(c), it is found that when there are both target arrival direction errors and amplitude-phase errors, the method of the present invention has good robustness to the number of snapshots and basically converges when the number of snapshots is 40 times.

[0161] As can be seen from the above description, an adaptive generation method disclosed by the present invention is based on a new feature projection of interference matrix estimation, and solves the problem of the decrease in output signal-to-interference-plus-noise ratio (SINR) caused by the mismatch of the target steering vector in adaptive beamforming technology. The implementation process is as follows: calculating the sample covariance matrix by using the training data collected by the array radar; reconstructing the interference matrix with the aid of the target blocking matrix and matrix feature transformation; estimating the target covariance matrix by removing the interference matrix from the sample covariance matrix; obtaining the signal subspace projection operator by eigenvalue decomposition of the target covariance matrix to complete the feature projection of the target prior steering vector; and finally calculating the weight vector by using the interference matrix and the target steering vector to realize beamforming. When there are both arrival angle and array calibration errors, the present invention improves the robustness of the beamformer and can be applicable to strong / weak target scenarios.

[0162] From the hardware level, in order to solve the problems that when there are constraint deviations in the target steering vector, main lobe offset and other problems may occur in the case of low signal-to-noise ratio (SNR), reducing the output SINR; in the case of high SNR, if the received data contains the target, there will even be a signal 'auto-cancellation' phenomenon, resulting in a sharp deterioration of the output SINR, this application provides an embodiment of an electronic device for implementing all or part of the content in the above generation method. The electronic device specifically includes the following content:

[0163] Figure 5 It is a schematic block diagram of the system composition of the electronic device 9600 according to an embodiment of the present application. As Figure 5 shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It should be noted that this Figure 5 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0164] In one embodiment, the adaptive beam generation may be integrated into the central processing unit. Among them, the central processing unit may be configured to perform the following controls:

[0165] Calculating the sample covariance matrix according to the collected sample data, where the sample data includes a target data and a plurality of interference data detected by the detection beam emitted by the radar echoer;

[0166] Processing the sample covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix;

[0167] The estimated target covariance matrix is obtained by subtracting the reconstructed interference matrix from the sampled covariance matrix;

[0168] A beam weight vector is obtained according to the estimated target covariance matrix and the reconstructed interference matrix;

[0169] The configuration parameters of the radar echoer are corrected according to the beam weight vector, so that the radar echoer generates a corrected adaptive beam.

[0170] In another embodiment, the generating device of the adaptive beam generating system can be separately configured from the central processor 9100. For example, the adaptive beam generating system can be configured as a chip connected to the central processor 9100, and the adaptive beam generating function is controlled through the central processor.

[0171] As Figure 5 shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily have to include Figure 5 all the components shown in Figure 5 ; in addition, the electronic device 9600 may further include

[0172] As Figure 5 shown, the central processor 9100 is sometimes also called a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 9100 receives inputs and controls the operations of the various components of the electronic device 9600.

[0173] Among them, the memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. The above information related to failures can be stored, and in addition, programs for executing relevant information can also be stored. And the central processor 9100 can execute the programs stored in the memory 9140 to implement information storage or processing, etc.

[0174] The input unit 9120 provides inputs to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display can be, for example, an LCD display, but is not limited thereto.

[0175] The memory 9140 can be a solid-state memory, for example, a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that stores information even when powered off, can be selectively erased, and has more data. An example of this memory is sometimes referred to as an EPROM, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage unit 9142, which is used to store application programs and function programs or the processes for operating the electronic device 9600 by the central processor 9100.

[0176] The memory 9140 can also include a data storage unit 9143, which is used to store data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 can include various drivers of the electronic device for communication functions and / or for performing other functions of the electronic device (such as a messaging application, an address book application, etc.).

[0177] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0178] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module (transmitter / receiver) 9110 is also coupled to the speaker 9131 and the microphone 9132 via the audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, so as to implement the usual telecommunication functions. The audio processor 9130 can include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that it is possible to record on the local machine through the microphone 9132 and play the sound stored on the local machine through the speaker 9131.

[0179] Embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the adaptive beam generation method in the above embodiments. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it implements all the steps of the adaptive generation method whose execution subject is a server or a client in the above embodiments. For example, when the processor executes the computer program, the following steps are implemented:

[0180] Calculate a sampling covariance matrix based on the collected sampling data, where the sampling data includes target data detected by a detection beam emitted by a radar echo device and multiple interference data;

[0181] Process the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix;

[0182] Subtract the reconstructed interference matrix from the sampling covariance matrix to obtain an estimated target covariance matrix;

[0183] Obtain a beam weight vector based on the estimated target covariance matrix and the reconstructed interference matrix;

[0184] Modify the configuration parameters of the radar echo device according to the beam weight vector so that the radar echo device generates a corrected adaptive beam.

[0185] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0186] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0187] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or boxes Figure 1 in one box or a plurality of boxes Figure 1 in the steps of the method.

[0189] In the present invention, specific embodiments are used to illustrate the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An adaptive beam generation method, characterized in that, Including: Calculating a sampling covariance matrix based on the collected sampling data, where the sampling data includes one target data and multiple interference data detected by a detection beam emitted by a radar echo device; Processing the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix; Subtracting the reconstructed interference matrix from the sampling covariance matrix to obtain an estimated target covariance matrix; Obtaining a beam weight vector based on the estimated target covariance matrix and the reconstructed interference matrix; Modifying the configuration parameters of the radar echo device according to the beam weight vector so that the radar echo device generates a modified adaptive beam; The adaptive beam generation method further includes: Collecting the sampling data at a set time in combination with a uniform linear array arranged with M half-wavelengths, where M = 10; The blocking matrix is formed by radar search configuration parameters according to the low-rank approximation rule.

2. The adaptive beam generation method according to claim 1, wherein The processing the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix includes: Processing the sampling covariance matrix with the blocking matrix to obtain a quasi-interference covariance matrix; Performing matrix eigen-transformation on partial principal eigenvectors of the quasi-interference covariance matrix to obtain the reconstructed interference matrix.

3. The adaptive beam generation method according to claim 1, wherein The obtaining a beam weight vector based on the estimated target covariance matrix and the reconstructed interference matrix includes: Processing the estimated target covariance matrix to obtain a target steering vector; Calculating the beam weight vector based on the target steering vector and the reconstructed interference matrix.

4. The adaptive beam generation method according to claim 3, wherein The processing the estimated target covariance matrix to obtain a target steering vector includes Performing eigenvalue decomposition on the estimated target covariance matrix to obtain the eigenvectors of the estimated target covariance matrix; Calculating the correlation coefficient between the eigenvectors and the prior steering vector of the radar target to determine the eigen-projection operator, and further obtaining the target steering vector.

5. An adaptive beam generation system, characterized in that, Including: Sampling covariance matrix module: Calculating a sampling covariance matrix based on the collected sampling data, where the sampling data includes one target data and multiple interference data detected by a detection beam emitted by a radar echo device; Reconstructed interference matrix module: Processing the sampling covariance matrix in combination with a blocking matrix to obtain a reconstructed interference matrix; Estimated target covariance matrix module: Subtracting the reconstructed interference matrix from the sampling covariance matrix to obtain an estimated target covariance matrix; Beam weight vector calculation module: Obtaining a beam weight vector based on the estimated target covariance matrix and the reconstructed interference matrix; Modifying module: Modifying the configuration parameters of the radar echo device according to the beam weight vector so that the radar echo device generates a modified adaptive beam; The adaptive beam generation system further includes: Sampling module: Collecting the sampling data at a set time in combination with a uniform linear array arranged with M half-wavelengths, where M = 10; The blocking matrix is formed by radar search configuration parameters according to the low-rank approximation rule.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the adaptive beam generation method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the adaptive beam generation method according to any one of claims 1 to 4.

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