Space-time adaptive anti-interference optimization method for multi-array-element antenna satellite navigation receiver

Abstract

The invention discloses a space-time adaptive anti-interference optimization method for a multi-array-element antenna satellite navigation receiver. The space-time adaptive anti-interference optimization method is used for the multi-array-element antenna satellite navigation receiver. According to the method, null broadening and deepening are carried out by using space and time domains, on the basis of minimum variance undistorted response criterion constraint, sampling signals are buffered and segmented in the time domain, the environment of large motion angle change and serious Doppler frequency offset of received interference signals can be better resisted, the SMI algorithm is adopted to replace a traditional LMS algorithm, and the method has the advantages of being high in robustness and high in robustness. The problems that the algorithm convergence speed is low and the actual application requirement cannot be met under the complex interference scene are solved through parallel computing.

Description

Technical Field

[0001] The invention relates to the field of satellite navigation anti-interference, and in particular to a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver. Background Art

[0002] After decades of development, the BeiDou satellite positioning system has become more complete, and how to better utilize BeiDou III signals has become a key research direction today. However, whether it is BeiDou II or III, as long as it is a satellite signal, it has its inherent defects, that is, it is easily affected by the environment and human interference signals, which leads to positioning and guidance failures. Therefore, all parties attach great importance to the research of anti-interference technology.

[0003] The continuous improvement of navigation systems and the ever-changing interference technology have also prompted anti-interference algorithms and anti-interference technologies to continuously respond to new challenges. Research on anti-interference algorithms usually starts from the three single-dimensional domains of time domain, frequency domain, and space domain. Now it has been expanded to the two-dimensional domains of space-time domain and space-frequency domain, and its anti-interference ability is constantly increasing. With the continuous development of anti-interference technology, adaptive zeroing technology, inertial navigation termination and digital beam synthesis comprehensive anti-interference have also emerged. With people's dependence on satellite navigation positioning, people are also forced to put forward more stringent requirements on the real-time and accuracy of the current position coordinates, thus putting forward higher requirements on the anti-interference of the receiver. The development of navigation receiver anti-interference technology has gradually become the focus of people's research.

[0004] Anti-interference technology in satellite navigation receivers can be mainly divided into three categories: antenna-based anti-interference technology, digital beam technology and inertial navigation auxiliary technology. Antenna-based anti-interference technology can be divided from different dimensions. Anti-interference technology is mainly divided into six types: time domain, frequency domain, spatial domain, space-time domain, space-frequency domain and polarization domain. For time domain filtering technology, the first step is to analyze the characteristics of the received navigation signal, and adjust the coefficient of the delay tap of the filter according to the signal characteristics to achieve the purpose of interference suppression. Time domain anti-interference technology can suppress many types of interference. Common time domain anti-interference methods include: adaptive FIR filter, adaptive IIR filter, and code-assisted technology. Frequency domain filtering technology analyzes the spectrum of the signal after Fourier transform, and then uses the spectrum excision method to achieve it. Processing methods based on transform domain include: FFT-based filtering, FRFT-based filtering, wavelet packet decomposition filtering, compressed sensing filtering, etc. Time domain filtering technology uses a single antenna to receive satellite signals, expands the single antenna and arranges it in different ways. This processing method is called spatial domain anti-interference technology. First, the antenna array is arranged, and then according to the different directions of the received navigation signal and the interference signal, the weight coefficients on each array element are adjusted to achieve the purpose of interference suppression. Single-dimensional domain anti-interference technology has its own advantages, so multi-dimensional domain anti-interference technology is a technology that combines them on this basis, and this method can achieve a better effect of suppressing interference. Methods based on array antenna processing include: adaptive zeroing, space-time adaptive filtering, space-frequency adaptive filtering, beamforming technology, etc. With the upgrading and gradual improvement of technology, polarization domain anti-interference technology has also become a new field for people to explore anti-interference.

[0005] However, the anti-interference performance of the commonly used anti-interference algorithms is greatly reduced in harsh environments such as high dynamics, and some existing algorithms with superior performance have too large a computational workload and only exist on simulation computing platforms, and cannot be used in actual engineering hardware platforms. Summary of the invention

[0006] The purpose of the present invention is to propose a buffered segmented space-time adaptive anti-interference algorithm that can run on a parallel processor in order to address the situation where the anti-interference performance is greatly reduced in harsh environments such as high dynamics, high overload and fast turns, and the calculation amount of some existing superior performance algorithms is too large to run on current mature processors.

[0007] The technical solution adopted by the present invention is:

[0008] A space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver, comprising:

[0009] (1) Use multi-element antennas to down-convert satellite navigation signals and perform analog-to-digital conversion;

[0010] (2) Use FIR bandpass filter to filter out DC components from the analog-to-digital converted signal and retain useful satellite signals;

[0011] (3) Using Hilbert transform to perform I / Q transformation on the useful satellite signal after filtering out the DC component to obtain a complex signal;

[0012] (4) performing buffered segmented space-time adaptive processing on the complex signal to achieve anti-interference;

[0013] (5) After the interference is counteracted, the signal is processed by digital AGC to achieve output power adjustment.

[0014] Furthermore, when the useful satellite signal after the DC component is filtered out is subjected to I / Q transformation by using Hilbert transformation, only orthogonal phase shift conversion is performed on it while keeping the amplitude of the spectrum component of the original signal unchanged.

[0015] Further, the complex signal is subjected to buffered segmented space-time adaptive processing, specifically: the complex signal is subjected to buffered segmented space-time adaptive processing by using an SMI algorithm, including:

[0016] Step 1: For the complex signal Calculate its covariance matrix in, The orthogonal down-conversion obtains the orthogonal complex signal, n represents the current time, H represents the conjugate transpose, σ 2 is the noise power, I is the unit matrix; K is the number of iterations;

[0017] Step 2: Perform an inverse operation on the covariance matrix to obtain an inverse matrix

[0018] Step 3: Calculate the optimal weighted vector according to the inverse matrix Where a(θ) is the steering vector of the desired signal direction, and θ∈[0,π / 2] is the beam pitch angle;

[0019] Step 4: Optimize the weight vector w d-opt Normalize, multiply by signal, output Among them, w opt is the result after normalization of the optimal weighted vector.

[0020] Furthermore, the inverse operation of step 2 is specifically performed by using LU triangular decomposition of the matrix;

[0021] First, perform LU triangular decomposition on the matrix, that is, decompose it into the product of a lower triangular matrix L with diagonal elements of 1 and an upper triangular matrix U;

[0022] Inverse the decomposed lower triangular matrix L and upper triangular matrix U;

[0023] By multiplying the inverse matrix of the L matrix and the inverse matrix of the U matrix, we can get the inverse of the original matrix.

[0024] Furthermore,

[0025] Perform LU triangular decomposition on the matrix, specifically:

[0026] If the n-order square matrix The order principal minors of are not equal to zero, that is:

[0027]

[0028] Then the LU decomposition of A = L × U exists and is unique;

[0029]

[0030] Based on the matrix multiplication principle, the iterative algorithm of LU decomposition is derived:

[0031] U 0j =a 0j ,j=0,1,2,…,n-1;

[0032]

[0033] The LU decomposition of a matrix is ​​a cyclic iterative process. The U matrix iterates from the 1st row to the nth row, while the L matrix iterates from the 1st column to the nth column, and the U matrix precedes the L matrix by one beat.

[0034] Furthermore, the decomposed lower triangular matrix L and upper triangular matrix U are inverted, specifically:

[0035] First, assume that the inverse matrix of the lower triangular matrix L is l, consider the case of order 4, and use Ll = I, we have:

[0036]

[0037] l 10 =-l 00 (l 11 L 10 )…;

[0038] l 20 =-l 00 (l 21 L 10 +l 22 L 20 )…;

[0039] l 30 =-l 00 (l 31 L 10+l 32 L 20 +l 33 L 30 )…;

[0040] Thus, the inverse matrix of the lower triangular matrix L is obtained as follows:

[0041]

[0042] The inverse matrix of the upper triangular matrix U is obtained as follows:

[0043]

[0044] Matrix inversion is an iterative process, which loops and iterates n-1 times to find the entire inverse matrix. The loop iteration of the U matrix is ​​in row order and column reverse order, and the loop iteration of the L matrix is ​​in column order and row order until all the results of the entire matrix are calculated.

[0045] Furthermore, the inverse matrix of the L matrix is ​​multiplied by the inverse matrix of the U matrix, specifically:

[0046] The inverse matrix u of the upper triangular matrix U is multiplied by the inverse matrix l of the lower triangular matrix L to finally obtain the inverse matrix A of the original matrix A. -1 =U -1 L -1 =ul, completing the entire matrix inversion process; the iterative form of n-order matrix multiplication can be expressed as follows:

[0047]

[0048] Furthermore, the step (3) calculates the optimal weighted vector according to the inverse matrix Specifically, the minimum variance distortion-free response criterion is adopted, and the weighted vector obtained by the minimum variance distortion-free response algorithm is used to minimize the array output power in the desired direction and maximize the signal-to-interference-noise ratio;

[0049]

[0050] Among them, P out is the power value of the array output in the desired direction, y(n) is the array antenna input value in the desired direction, E is the mathematical expectation, and w is the weighting vector;

[0051] The optimal weighted vector can be obtained:

[0052]

[0053] In a second aspect, the present invention further proposes a non-volatile storage medium, comprising: a computer program product, which, when executed, executes the space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver.

[0054] In a third aspect, the present invention further proposes a computer program product, which, when executed by a processor, implements the space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver.

[0055] The beneficial effects brought by the present invention compared with the prior art are as follows:

[0056] (1) In order to solve the problem that the signal loss in the communication direction is too large and the signal in the interference direction cannot be minimized, the present invention provides a beamforming algorithm suitable for using the minimum variance distortionless response criterion. The minimum variance distortionless response criterion is based on the optimal signal-to-interference ratio criterion. The weighted vector obtained by the minimum variance distortionless response algorithm can minimize the array output power in the desired direction and maximize the signal-to-interference-noise ratio.

[0057] (2) The existing mature algorithms have poor dynamic performance. Under conditions such as severe vibration and rapid turning, the performance will be greatly reduced. The present invention provides an SMI algorithm that can be used for parallel processor implementation. When there is coherent interference or the number of interferences is small, the covariance matrix is ​​close to singular. In order to ensure the reversibility of the matrix, a diagonal loading technique is usually used.

[0058] (3) In order to improve the anti-interference performance, the present invention provides a buffered segmented space-time adaptive anti-interference algorithm, which performs buffered segmented processing on the sampled signal in the time domain on the basis of the minimum variance distortion-free response criterion constraint, and uses the Lagrange multiplier method to derive the solution of the optimal space-time processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Based on buffer segmented space-time adaptive anti-interference flow chart;

[0060] Figure 2 It is a schematic diagram of the adaptive array antenna anti-interference processing system;

[0061] Figure 3 It is a block diagram of the matrix inversion algorithm based on parallel processors. DETAILED DESCRIPTION

[0062] The present invention aims at the situations where the anti-interference performance is greatly reduced in harsh environments such as high dynamics, high overload and fast turns, and the calculation amount of some existing algorithms with superior performance is too large to run on current mature processors. Therefore, a buffered segmented space-time adaptive anti-interference algorithm that can run on parallel processors is proposed.

[0063] like Figure 1 As shown, the present invention proposes a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver, comprising the following steps:

[0064] (1) Use multi-element antennas to down-convert satellite navigation signals and perform analog-to-digital conversion;

[0065] (2) Use FIR bandpass filter to filter out DC components from the analog-to-digital converted signal and retain useful satellite signals;

[0066] (3) After filtering out the DC component, the useful satellite signal is subjected to I / Q transformation using Hilbert transform to obtain a complex signal; while keeping the amplitude of the original signal spectrum component unchanged, only the orthogonal phase shift conversion is performed on it.

[0067] (4) performing buffered segmented space-time adaptive processing on the complex signal to achieve anti-interference (performing buffered segmented space-time adaptive processing on the complex signal through the SMI algorithm);

[0068] (5) After the interference is counteracted, the signal is processed by digital AGC to achieve output power adjustment.

[0069] Adaptive array antenna anti-interference processing system Figure 2 As shown, it consists of a receiving antenna array unit and an adaptive processor. Where M is the number of antenna array elements, x m (n) is the signal from the mth antenna element, is the complex weighting coefficient. After the antenna array receives the signal, the adaptive processor adjusts the complex weighting coefficient. The processing of the weighting coefficient is very important, and it plays an important role in the beam null formation of the system.

[0070] The output y(n) of the beamformer at time n is a linear combination of the output data of the M array elements at that time.

[0071] y(n)=w H x(n)

[0072] Where w=[w1,w2,...w M ] T It is called a weighted vector. Each element of the weighted vector is a complex number, its modulus represents the amplitude weighting of the array element output signal, and its phase angle represents the phase delay of the array element output signal. The output power after anti-interference is:

[0073] P out =E[y(n) 2 ]=w H E[x(n)x H (n)]w=w H R xx w

[0074] Where R xx =E[x(n)x H (n)] is the input signal autocorrelation matrix.

[0075] The beamforming algorithm based on minimum variance distortion-free response in the present invention needs to form a digital beam so as to minimize the signal loss in the desired communication direction and at the same time minimize the signal in the interference direction.

[0076] When there is coherent interference or the number of interferers is small, the covariance matrix It is close to singular. In order to ensure the reversibility of the matrix, diagonal loading technology is usually used.

[0077] like Figure 3 As shown, the complex signal is buffered and segmented space-time adaptively processed by the SMI algorithm, which specifically includes the following steps:

[0078] Step 1: For the complex signal Calculate its covariance matrix in, The orthogonal down-conversion obtains the orthogonal complex signal, n represents the current time, H represents the conjugate transpose, σ 2 is the noise power, I is the unit matrix; K is the number of iterations;

[0079] Step 2: Perform an inverse operation on the covariance matrix to obtain an inverse matrix

[0080] Step 3: Calculate the optimal weighted vector according to the inverse matrix Where a(θ) is the steering vector of the desired signal direction, and θ∈[0,π / 2] is the beam pitch angle;

[0081] Step 4: Optimize the weight vector w d-opt Normalize, multiply by signal, output Among them, w opt is the result after normalization of the optimal weighted vector.

[0082] One of its core key technologies is the matrix inversion algorithm based on parallel processors.

[0083] Under absolutely ideal hardware conditions such as antennas and RF channels, and when the signal bandwidth is not large, excellent anti-interference performance can be achieved by using only a pure spatial adaptive zeroing algorithm. However, actual antennas are subject to inconsistent directional patterns, mutual coupling, and other influences, and the RF channel is also inconsistent. When the signal bandwidth is large, if only the spatial adaptive anti-interference algorithm is used, the anti-interference performance will be severely reduced. Therefore, a space-time adaptive anti-interference algorithm or a space-frequency adaptive anti-interference algorithm is generally used to improve the anti-interference performance.

[0084] The structure of the space-frequency anti-interference algorithm is relatively complex. The wideband signal received by the array is decomposed into narrowband signals through FFT, and the narrowband signal in each sub-band is subjected to anti-interference. The anti-interference output signal in the frequency domain of each sub-band is combined to synthesize the final anti-interference frequency domain output signal, and then the anti-interference signal in the time domain is obtained through IFFT.

[0085] The present invention performs buffer segmentation processing on the sampled signal in the time domain on the basis of the minimum variance distortion-free response criterion constraint to improve the anti-interference performance, that is, a space-time adaptive anti-interference algorithm. m (n)}, n=0,2,…,N-1; m=1,2…,M are its space-time two-dimensional weight coefficients. The buffer time of each beat is T, which is required to be less than 1 / B (B is the processing bandwidth); the total delay length of each array element signal is (N-1)T.

[0086] The processor weight vector is represented by MN×1 dimensional vector W, R=E[XX H ] is the covariance matrix of the received data (MN×MN dimension), and the processor can be described as a constrained optimization problem:

[0087]

[0088] S is the space-time two-dimensional derivative vector. Using the Lagrange multiplier method, the solution of the optimal space-time processor can be derived as:

[0089]

[0090] At this point, some familiar adaptive methods and their various variants can be used to adaptively solve the above equation. In this system design, the power inversion criterion is adopted to solve the above equation.

[0091] The SMI algorithm uses the collected data to estimate the covariance matrix R and inverts R. According to the formula One of the key technologies of the SMI algorithm is the matrix inversion algorithm based on parallel processors.

[0092] The inverse operation described in the present invention specifically adopts LU triangular decomposition of the matrix; an n-order matrix A is decomposed into the product of a lower triangular matrix L and an upper triangular matrix U.

[0093] (1) First, perform LU triangular decomposition on the matrix, that is, decompose it into the product of a lower triangular matrix L with diagonal elements of 1 and an upper triangular matrix U;

[0094] (2) Invert the decomposed lower triangular matrix L and upper triangular matrix U;

[0095] (3) By multiplying the inverse matrix of the L matrix and the inverse matrix of the U matrix, we can get the inverse of the original matrix.

[0096] Specifically, the matrix is ​​decomposed into LU triangles, as follows:

[0097] If the n-order square matrix The order principal minors of are not equal to zero, that is:

[0098]

[0099] Then the LU decomposition of A = L × U exists and is unique;

[0100]

[0101] Based on the matrix multiplication principle, the iterative algorithm of LU decomposition is derived:

[0102] U 0j =a 0j ,j=0,1,2,…,n-1;

[0103]

[0104] The LU decomposition of a matrix is ​​a cyclic iterative process. The U matrix iterates from the 1st row to the nth row, while the L matrix iterates from the 1st column to the nth column, and the U matrix precedes the L matrix by one beat.

[0105] Specifically, the decomposed lower triangular matrix L and upper triangular matrix U are inverted as follows:

[0106] First, assume that the inverse matrix of the lower triangular matrix L is l, consider the case of order 4, and use Ll = I, we have:

[0107]

[0108] l 10 =-l 00 (l 11 L 10 )…;

[0109] l 20 =-l 00 (l 21 L 10 +l 22 L 20 )…;

[0110] l 30 =-l 00 (l 31 L 10 +l 32 L 20 +l 33 L 30 )…;

[0111] Thus, the inverse matrix of the lower triangular matrix L is obtained as follows:

[0112]

[0113] The inverse matrix of the upper triangular matrix U is obtained as follows:

[0114]

[0115] Matrix inversion is an iterative process, which loops and iterates n-1 times to find the entire inverse matrix. The loop iteration of the U matrix is ​​in row order and column reverse order, and the loop iteration of the L matrix is ​​in column order and row order until all the results of the entire matrix are calculated.

[0116] Specifically, the inverse matrix of the L matrix is ​​multiplied by the inverse matrix of the U matrix, specifically:

[0117] The inverse matrix u of the upper triangular matrix U is multiplied by the inverse matrix l of the lower triangular matrix L to obtain the inverse matrix A of the original matrix A. -1 =U -1 L -1 =ul, completing the entire matrix inversion process; the iterative form of n-order matrix multiplication can be expressed as follows:

[0118]

[0119] The step (3) calculates the optimal weighted vector according to the inverse matrix Specifically, the minimum variance distortion-free response criterion is adopted, and the weighted vector obtained by the minimum variance distortion-free response algorithm is used to minimize the array output power in the desired direction and maximize the signal-to-interference-noise ratio;

[0120]

[0121] Among them, P out is the power value of the array output in the desired direction, y(n) is the array antenna input value in the desired direction, E is the mathematical expectation, and w is the weighting vector;

[0122] The optimal weighted vector can be obtained:

[0123]

[0124] The method of the present invention can better resist the environment with large motion angle changes and severe Doppler frequency deviation of the received interference signal. The SMI algorithm is used to replace the traditional LMS algorithm, and parallel computing is used to solve the problem of slow algorithm convergence speed and inability to meet actual application needs in complex interference scenarios.

[0125] Parts of the present invention that are not described in detail belong to common knowledge among those skilled in the art.

Claims

1. A space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver, characterized in that include: Use multi-element antennas to down-convert satellite navigation signals and perform analog-to-digital conversion; Use FIR bandpass filter to filter out DC component of the signal after analog-to-digital conversion and retain useful satellite signals; The useful satellite signal after the DC component is filtered out is subjected to I / Q transformation using Hilbert transform to obtain a complex signal; Performing buffered segmented space-time adaptive processing on the complex signal to achieve anti-interference; The signal after counteracting interference completes digital AGC processing to achieve output power adjustment.

2. According to claim 1, a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver is characterized by: When the useful satellite signal after the DC component is filtered out is subjected to I / Q transformation by Hilbert transform, only the orthogonal phase shift conversion is performed on it while keeping the amplitude of the original signal spectrum component unchanged.

3. According to claim 1, a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver is characterized by: The complex signal is subjected to buffered segmented space-time adaptive processing, specifically: the complex signal is subjected to buffered segmented space-time adaptive processing by using an SMI algorithm, including: Step 1: For the complex signal Calculate its covariance matrix in, The orthogonal down-conversion obtains the orthogonal complex signal, n represents the current time, H represents the conjugate transpose, σ 2 is the noise power, I is the unit matrix; K is the number of iterations; Step 2: Perform an inverse operation on the covariance matrix to obtain an inverse matrix Step 3: Calculate the optimal weighted vector according to the inverse matrix Where a(θ) is the steering vector of the desired signal direction, and θ∈[0,π / 2] is the beam pitch angle; Step 4: Optimize the weight vector w d-opt Normalize, multiply by signal, output Among them, w opt is the result after normalization of the optimal weighted vector.

4. According to claim 3, a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver is characterized in that: The step 2 is to perform an inverse operation, specifically using LU triangular decomposition of the matrix; First, perform LU triangular decomposition on the matrix, that is, decompose it into the product of a lower triangular matrix L with diagonal elements of 1 and an upper triangular matrix U; Inverse the decomposed lower triangular matrix L and upper triangular matrix U; By multiplying the inverse matrix of the L matrix and the inverse matrix of the U matrix, we can get the inverse of the original matrix.

5. According to claim 4, a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver is characterized in that: Perform LU triangular decomposition on the matrix, specifically: If the n-order square matrix The order principal minors of are not equal to zero, that is: Then the LU decomposition of A = L × U exists and is unique; Based on the matrix multiplication principle, the iterative algorithm of LU decomposition is derived: U 0j =a 0j ,j=0,1,2,…,n-1; The LU decomposition of a matrix is ​​a cyclic iterative process. The U matrix iterates from the 1st row to the nth row, while the L matrix iterates from the 1st column to the nth column, and the U matrix precedes the L matrix by one beat.

6. The space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver according to claim 5, characterized in that: The decomposed lower triangular matrix L and upper triangular matrix U are inverted as follows: First, assume that the inverse matrix of the lower triangular matrix L is l, consider the case of order 4, and use Ll = I, we have: l 10 ＝-l 00 (l 11 L 10 )…； L 20 =-l 00 (L 21 L 10 +l 22 L 20 )…; L 30 =-l 00 (L 31 L 10 +l 32 L 20 +l 33 L 30 )…; Thus, the inverse matrix of the lower triangular matrix L is obtained as follows: The inverse matrix of the upper triangular matrix U is obtained as follows: Matrix inversion is an iterative process, which loops and iterates n-1 times to find the entire inverse matrix. The loop iteration of the U matrix is ​​in row order and column reverse order, and the loop iteration of the L matrix is ​​in column order and row order until all the results of the entire matrix are calculated.

7. The space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver according to claim 6, characterized in that: The inverse matrix of the L matrix is ​​multiplied by the inverse matrix of the U matrix, specifically: The inverse matrix u of the upper triangular matrix U is multiplied by the inverse matrix l of the lower triangular matrix L to finally obtain the inverse matrix A of the original matrix A. -1 =U -1 L -1 =ul, completing the entire matrix inversion process; the iterative form of n-order matrix multiplication can be expressed as follows:

8. According to claim 3, a space-time adaptive anti-interference optimization method for a multi-element antenna satellite navigation receiver is characterized by: The step (3) calculates the optimal weighted vector according to the inverse matrix Specifically, the minimum variance distortion-free response criterion is adopted, and the weighted vector obtained by the minimum variance distortion-free response algorithm is used to minimize the array output power in the desired direction and maximize the signal-to-interference-noise ratio; Among them, P out is the power value of the array output in the desired direction, y(n) is the array antenna input value in the desired direction, E is the mathematical expectation, and w is the weighting vector; The optimal weighted vector can be obtained:

9. A non-volatile storage medium, characterized in that: include: A computer program product, when the computer program product is executed, performs the method according to any one of claims 1 to 8.

10. A computer program product, characterized in that When the computer program product is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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