Self-adaptive signal beam forming method and device, equipment and storage medium

Abstract

The invention provides an adaptive signal beam forming method and device, equipment and a storage medium. The method receives signals based on a uniform linear array of 2N + 1 array elements. The method comprises the following steps: constructing multiple Toeplitz matrixes, and combining the multiple Toeplitz matrixes to obtain a covariance matrix; calculating a signal steering vector according to the characteristic value and the characteristic vector, and optimizing to obtain an expected signal steering vector; estimating noise power by using the minimum eigenvalue, establishing a corresponding relation between signal power and eigenvectors, and determining expected signal power; reconstructing a covariance matrix of uncorrelated noise according to the noise power, the corresponding relation and the expected signal power; and substituting the desired signal steering vector and the reconstructed covariance matrix into a Cap on beam former, and calculating an optimal weight vector to control beam forming. According to the method, multiple Toepli tz matrixes are constructed, and the characteristics of the multiple Toepli tz matrixes are utilized to carry out signal decoherence and covariance matrix reconstruction, so that the performance and reliability of a beam forming system in a complex signal environment are improved.

Description

Technical Field

[0001] The present application belongs to the field of signal beam control, and in particular, relates to an adaptive signal beamforming method, device, equipment and storage medium. Background Art

[0002] In the field of signal processing technology, beamforming is an important technology that dynamically adjusts the direction of the beam according to the signal environment, thereby enhancing the desired signal and suppressing interference. However, traditional beamforming algorithms have difficulties in processing coherent signals, especially when the subarray length is not enough to distinguish coherent signals or the signal-to-noise ratio (SNR) is low, its performance will be significantly affected. The presence of coherent signals will lead to increased correlation of the array received signals, making it difficult for traditional algorithms to accurately distinguish and process multiple signal sources.

[0003] To overcome this problem, researchers have proposed various improvement methods. Among them, the adaptive beamforming method based on Toeplitz matrix reconstruction is an effective solution. However, the existing beamforming methods based on Toeplitz matrix still have certain limitations, such as the need to pre-process the received array noise or the processing accuracy is not high enough in low SNR environment. Summary of the invention

[0004] The purpose of the present application is to overcome the above-mentioned prior art and provide an adaptive signal beamforming method, device, equipment and storage medium.

[0005] The present application provides an adaptive signal beamforming method, comprising:

[0006] Construct multiple Toeplitz matrices based on the signals received by the ULA array of 2N+1 elements;

[0007] Combining the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors;

[0008] Calculating a steering vector of the signal according to the eigenvalue and the eigenvector;

[0009] Optimizing the steering vector using a preset optimization function to obtain a desired signal steering vector;

[0010] estimating noise power using the minimum eigenvalue of the covariance matrix;

[0011] Establishing a corresponding relationship between the signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector;

[0012] Determine the expected signal power according to the corresponding relationship;

[0013] Reconstructing a covariance matrix of mutually uncorrelated noises according to the noise power, the corresponding relationship and the expected signal power;

[0014] Bringing the desired signal steering vector and the covariance matrix of the uncorrelated noise into a Capon beamformer to calculate an optimal weight vector;

[0015] The beamforming of the signal is controlled according to the optimal weight vector.

[0016] Optionally, a multiple Toeplitz matrix is ​​constructed based on the signal received by the ULA array of 2N+1 elements, including:

[0017] Taking each array element in the ULA array of the 2N+1 array elements as a reference array element, and using a signal received by each array element as a reference signal;

[0018] The received signals of all array elements are obtained in sequence, and multiple Toeplitz matrices under all reference array elements are repeatedly constructed.

[0019] Optionally, the multiple Toeplitz matrices are combined by a square sum operation to obtain a covariance matrix, which is expressed as follows:

[0020]

[0021] Among them, R m is a Toeplitz matrix, and N is the array element number.

[0022] Optionally, calculating the steering vector of the signal according to the eigenvalue and the eigenvector includes:

[0023] The eigenvalues ​​and eigenvectors of the covariance matrix are obtained by eigendecomposition, and the direction of arrival (DOA) of the signal is estimated using the MUSIC algorithm to reconstruct the steering vector.

[0024] Optionally, determining the expected signal power according to the corresponding relationship includes:

[0025] According to the corresponding relationship, after all steering vectors are matched with signal powers, the expected signal power is placed in the first column, and the expected signal power is obtained by calculating the non-zero elements in the first column of the diagonal matrix.

[0026] Optionally, reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship, and the expected signal power includes:

[0027] Deleting the desired signal power to obtain a diagonal matrix without the desired signal power;

[0028] An inverse operation is performed on the diagonal matrix to obtain a reconstructed mutually uncorrelated noise covariance matrix.

[0029] Optionally, it is applied to the control of communication beams or radar beams.

[0030] The present application also provides an adaptive signal beamforming device, comprising:

[0031] The matrix module constructs a multi-Toeplitz matrix based on the signals received by the ULA array of 2N+1 elements;

[0032] A merging module, merging the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors;

[0033] A calculation module, which calculates a steering vector of the signal according to the eigenvalue and the eigenvector;

[0034] An optimization module, which optimizes the steering vector using a preset optimization function to obtain a desired signal steering vector;

[0035] An estimation module, using the minimum eigenvalue of the covariance matrix to estimate noise power;

[0036] A relationship module, which establishes a corresponding relationship between the signal power and the feature vector according to the correlation between the feature vector and the desired signal steering vector;

[0037] A determination module, which determines the expected signal power according to the corresponding relationship;

[0038] A reconstruction module, reconstructing a covariance matrix of mutually uncorrelated noises according to the noise power, the corresponding relationship and the expected signal power;

[0039] A weight vector module, which brings the desired signal steering vector and the covariance matrix of the uncorrelated noise into a Capon beamformer to calculate an optimal weight vector;

[0040] A control module controls the beamforming of the signal according to the optimal weight vector.

[0041] Optionally, a multiple Toeplitz matrix is ​​constructed based on the signal received by the ULA array of 2N+1 elements, including:

[0042] Taking each array element in the ULA array of the 2N+1 array elements as a reference array element, and using a signal received by each array element as a reference signal;

[0043] The received signals of all array elements are obtained in sequence, and multiple Toeplitz matrices under all reference array elements are repeatedly constructed.

[0044] Optionally, the multiple Toeplitz matrices are combined by a square sum operation to obtain a covariance matrix, which is expressed as follows:

[0045]

[0046] Among them, R m is a Toeplitz matrix, and N is the array element number.

[0047] Optionally, calculating the steering vector of the signal according to the eigenvalue and the eigenvector includes:

[0048] The eigenvalues ​​and eigenvectors of the covariance matrix are obtained by eigendecomposition, and the direction of arrival (DOA) of the signal is estimated using the MUSIC algorithm to reconstruct the steering vector.

[0049] Optionally, determining the expected signal power according to the corresponding relationship includes:

[0050] According to the corresponding relationship, after all steering vectors are matched with signal powers, the expected signal power is placed in the first column, and the expected signal power is obtained by calculating the non-zero elements in the first column of the diagonal matrix.

[0051] Optionally, reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship, and the expected signal power includes:

[0052] Deleting the desired signal power to obtain a diagonal matrix without the desired signal power;

[0053] An inverse operation is performed on the diagonal matrix to obtain a reconstructed mutually uncorrelated noise covariance matrix.

[0054] Optionally, it is applied to the control of communication beams or radar beams.

[0055] The present application provides an adaptive signal beamforming device, comprising:

[0056] A memory, used for storing a computer executable program of the above-mentioned adaptive signal beamforming method;

[0057] A processor is used to retrieve the computer executable program from the memory and execute: construct multiple Toeplitz matrices based on signals received by a ULA array of 2N+1 array elements; merge the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors; calculate a steering vector of the signal according to the eigenvalues ​​and the eigenvectors; optimize the steering vector using a preset optimization function to obtain a desired signal steering vector; estimate noise power using the minimum eigenvalue of the covariance matrix; establish a corresponding relationship between signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector; determine the desired signal power according to the corresponding relationship; reconstruct a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the desired signal power; bring the desired signal steering vector and the covariance matrix of the mutually uncorrelated noise into a Capon beamformer to calculate an optimal weight vector; and control the beamforming of the signal according to the optimal weight vector.

[0058] The present application also provides a storage medium, comprising: storing a computer executable program, wherein the computer executable program is used to be called by a processor to execute the steps of the above-mentioned adaptive signal beamforming method.

[0059] The beneficial effects of this application are:

[0060] The present application provides an adaptive signal beamforming method, comprising: constructing multiple Toeplitz matrices based on signals received by a UL A array of 2N+1 array elements; merging the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors; calculating a steering vector of the signal according to the eigenvalues ​​and the eigenvectors; optimizing the steering vector using a preset optimization function to obtain an expected signal steering vector; estimating noise power using the minimum eigenvalue of the covariance matrix; establishing a corresponding relationship between signal power and the eigenvector according to the correlation between the eigenvector and the expected signal steering vector; determining the expected signal power according to the corresponding relationship; reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the expected signal power; bringing the expected signal steering vector and the covariance matrix of the mutually uncorrelated noise into a Capon beamformer to calculate an optimal weight vector; and controlling the beamforming of the signal according to the optimal weight vector. The present application is based on the signal received by a uniform linear array (ULA) with 2N+1 array elements. By constructing multiple Toeplitz matrices and utilizing their characteristics to perform signal decorrelation and covariance matrix reconstruction, the performance and reliability of the beamforming system in a complex signal environment are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the adaptive signal beamforming process in this application;

[0062] Figure 2 It is a schematic diagram of the array element in this application;

[0063] Figure 3 It is a schematic diagram of an adaptive signal beamforming device in this application. DETAILED DESCRIPTION

[0064] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that various forms of implementing the present disclosure should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0065] Please refer to Figure 1 As shown, an adaptive signal beamforming method, the method comprises the following steps:

[0066] S101, constructing a multiple Toeplitz matrix based on the signal received by the ULA array of 2N+1 array elements;

[0067] In the field of radar or communication, the array element array for receiving signals is a ULA array with 2N+1 elements, and the array is completely symmetrical around the central element, such as Figure 2 shown.

[0068] Suppose there are M incident signals, among which there are K coherent signals and the rest are independent of each other.

[0069] According to the definition of coherent signal, the array receiving signal is expressed as:

[0070]

[0071] The formula is expressed as a vector:

[0072] x(t)=As(t)+n(t) (2)

[0073] Among them, β i is the correlation coefficient between the ith signal and the first signal, x(t) is the (2N+1)×1 receiving vector, A is the (2N+1)×M array flow matrix, A=[a1,…,a K ,a K+1 ,…,a M ], s(t) is an M×1 signal vector, and n(t) is a (2N+1)×1 noise vector.

[0074] According to the above formula, assuming that the central array element is the reference array element, the received signal of the kth array element is expressed as:

[0075]

[0076] Where k = -N,…,0,…,N.

[0077] When the central array element is the reference array element, the signal x0(t) it receives is called the reference signal. Based on the reference signal, the received signal matrix of all other array elements is obtained as follows:

[0078]

[0079] in, represents the received signal of the kth array element relative to the reference array element, so it can be derived from formula (3):

[0080]

[0081] Since the first K signals are coherent and the last MK signals are independent, the noises are also independent of each other, and the noise and the signal are also independent of each other, so:

[0082]

[0083] It can be seen that Combined with formula (3), formula (5) is simplified to:

[0084]

[0085] The last term in formula (6) has a value only when k=0, otherwise it is 0. Specifically:

[0086]

[0087] Where δ(k) represents a step function, δ(0) = 1, and the value is 0 at other times. represents the sum of all correlation coefficients, then formula (6) can be rewritten as:

[0088]

[0089] in, Indicates the signal power.

[0090] Substituting formula (8) into (4) we can express Y0 as:

[0091] Y0=A0f+n0 (9)

[0092] Among them, A0 is a column-full rank matrix composed of array steering vectors, and the specific form is:

[0093]

[0094] f is a column matrix consisting of power and correlation coefficients, and its specific form is:

[0095]

[0096] n0 is a (2N+1)×M dimensional matrix in which only the position k=0 is 1 and the other positions are 0.

[0097] The Y0 matrix at this time is similar to the matrix generated by single snapshot data. If the covariance is directly calculated for formula (9), its rank is still not satisfied.

[0098] According to the definition of the Toeplitz matrix, the value of Y0 is used to construct the Toeplitz matrix in order to reconstruct the covariance matrix into a full-rank matrix, as follows:

[0099]

[0100] Among them, B0 is the half-array array flow matrix, specifically:

[0101] B0=[a1,…,a K ,a K+1 ,…,a M ] (13)

[0102] And a i is the steering vector of the i-th signal, that is:

[0103]

[0104] Where F is a diagonal matrix with the elements in f as diagonal elements. (N+1) It is the (N+1)×(N+1) identity matrix.

[0105] Since the matrix B0 is a full-rank Vandermonde matrix, the rank of the signal covariance matrix satisfies:

[0106]

[0107] That is, according to formula (12), the covariance matrix can be restored to full rank using Toeplitz reconstruction, thus achieving signal decorrelation.

[0108] S102, combining the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors;

[0109] In step S101, only x0(t) is used as the reference signal. In order to make full use of the signal information and noise information in the array and increase the completeness of decorrelation, x m (t), m = -N, ..., 0, ..., N as reference signals, repeat the above steps, specifically:

[0110] When the reference signal is x m (t), formula (4) is rewritten as:

[0111]

[0112] At this time, formula (16) is also rewritten as:

[0113]

[0114] in, Indicates x m (t) is the received signal of the kth array when it is the reference signal.

[0115] Formula (17) can be expressed as formula (8):

[0116]

[0117] Among them, δ(km) is a step function, that is:

[0118]

[0119] From formula (18), we can see that the signal and noise power are independent of the value of m. Compared to Only the natural logarithm and the step function values ​​change. Similarly, formula (18) can be expressed in matrix form:

[0120] Y m =A m f+n m (20)

[0121] in,

[0122]

[0123] Where D is a diagonal matrix, that is

[0124]

[0125] Among them, f is consistent with the result of formula (21), n m It represents a (2N+1)×M matrix that is 1 only when k=m and 0 otherwise.

[0126] Y mReconstructed into a Teoplitz matrix, that is:

[0127]

[0128] Among them, I (N+1),m It represents a (N+1)×(N+1) matrix in which only the value of the mth diagonal is 1 and the rest are 0. When m is less than 0, the position of 1 is above the main diagonal, otherwise it is below the main diagonal. In particular, when m=0, it represents the identity matrix.

[0129] According to formula (23), all reconstructed Toeplitz matrices are full rank, and the rank value is the number of signals, so when using them to reconstruct the covariance matrix, the signal decorrelation is completed. After obtaining the Toeplitz matrices under all reference array elements, they are summed by square to obtain the final covariance matrix, specifically:

[0130]

[0131] The covariance matrix obtained by expanding multiple Toeplitz matrices utilizes the relevant information of all signals in the spatial array. In addition, the covariance matrix obtained by square sum operation has better decorrelation effect, does not require noise preprocessing, and has a more obvious decorrelation effect in low signal-to-noise ratio environments.

[0132] S103, calculating a steering vector of the signal according to the eigenvalue and the eigenvector;

[0133] After the final covariance matrix is ​​calculated by formula (24), the covariance matrix is ​​eigendecomposed to obtain its eigenvalues ​​and eigenvectors. Specifically:

[0134]

[0135] Among them, λ k are the eigenvalues ​​arranged from large to small, e k is the eigenvector corresponding to the kth eigenvalue, represents the power of the kth signal, a k is the steering vector of the kth signal, is the noise power.

[0136] S104, optimizing the steering vector using a preset optimization function to obtain a desired signal steering vector;

[0137] After eigendecomposition, the signal DOA is estimated by the MUSIC algorithm, and then the signal steering vector is calculated by formula (26), which is expressed as follows:

[0138]

[0139] However, in practical applications, the steering vector is affected by a variety of error factors and is prone to mismatch. Therefore, the steering vector needs to be corrected. According to the knowledge of matrix space, the desired signal steering vector and its corresponding eigenvector are in the same subspace, and their inner product is the largest when the steering vector is not mismatched. Therefore, the following optimization function is constructed to correct the steering vector. The optimization function expression is as follows:

[0140]

[0141] Where <·> represents the inner product operation, a1 represents the desired signal steering vector, and ||·||2 represents the -2 norm. Substituting the N+1 eigenvectors obtained by eigendecomposition of formula (25) into formula (27), we get N+1 eigenvectors about e k Inner product operation value. And just need to find the largest of these values, which means the correlation between the two is the largest, and at this time it means that the feature vector and the expected signal steering vector are in a corresponding state.

[0142] Because the steering vectors of different signals are orthogonal to each other, we have:

[0143]

[0144] Similarly, the expected signal steering vector and the corresponding eigenvector also satisfy the above equation, namely:

[0145]

[0146] Finally, the corrected desired signal steering vector is obtained as:

[0147]

[0148] The corrected steering vector of formula (30) is the steering vector from the 0th element to the Nth element. If you want to get the steering vector from the -Nth element to the 0th element, you only need to take the conjugate flip of the above result. So the final desired signal steering vector can be obtained by superimposing the two.

[0149] S05, using the minimum eigenvalue of the covariance matrix to estimate noise power;

[0150] According to the characteristic decomposition of the covariance matrix, we can know that:

[0151]

[0152] Since the coherent signal does not change the noise space, the smallest N+1-M eigenvalue is still used to estimate the noise power, that is:

[0153]

[0154] S106, establishing a corresponding relationship between the signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector;

[0155] Since the expansion of multiple Topelitz matrices may lead to changes in the signal space, there is no one-to-one correspondence between the steering vector and its power. Therefore, the following correlation function is defined:

[0156]

[0157] Among them, E s represents the signal subspace, that is, the linear subspace spanned by the first M eigenvectors. k Substitute them into the signal subspace respectively. If the eigenvector corresponds to the steering vector, their correlation function value is the largest. This step can establish a connection between the steering vector and the eigenvector. The eigenvalue and the eigenvector are in one-to-one correspondence, so it is equivalent to a one-to-one correspondence between the steering vector and the power.

[0158] S107, determining the expected signal power according to the corresponding relationship;

[0159] After all steering vectors are matched to signal powers, the desired signal power is placed in the first column and the decomposition result of formula (31) is rearranged:

[0160]

[0161] Wherein, Σ represents a diagonal matrix composed of all signal powers, B is composed of steering vectors with an array element length of N+1, and the first column is the desired signal steering vector.

[0162] All steering vectors are expanded to 2N+1 using formula (35):

[0163]

[0164] Reconstruct the covariance matrix using the expanded steering vector:

[0165]

[0166] Where A is the array flow matrix composed of the steering vectors in (35). Therefore, the diagonal matrix is:

[0167]

[0168] In formula (37), the non-zero elements in the first column of the diagonal matrix Σ are the desired signal powers. By deleting them, we can obtain a diagonal matrix without the desired signal power.

[0169] S108, reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the expected signal power;

[0170] The inverse operation of (37) can be used to obtain the covariance matrix of the reconstructed mutually uncorrelated noise (IPN CM), that is:

[0171]

[0172] S109, bringing the desired signal steering vector and the covariance matrix of the uncorrelated noise into a Capon beamformer to calculate an optimal weight vector;

[0173] Finally, the desired signal steering vector and the reconstructed IPNCM are brought into the Capon beamformer to obtain the optimal weight vector, which is expressed as follows:

[0174]

[0175] S110. Control beamforming of the signal according to the optimal weight vector.

[0176] The signal received by each antenna element (or the signal to be transmitted) is multiplied by the corresponding weight to adjust the phase and / or amplitude of the signal.

[0177] By applying an optimal weight vector, the shape and direction of the beam can be adjusted to achieve specific communication or radar objectives.

[0178] In wireless communications, a directional beam is formed toward a specific user device to enhance signal strength and reduce interference.

[0179] In radar systems, a narrow beam is formed to pinpoint a target, or a wide beam is formed to cover a wider search area.

[0180] Furthermore, in practical applications, it is necessary to constantly monitor the effect of beamforming and adjust the optimal weight vector as needed.

[0181] Application scenarios:

[0182] Wireless communication: used to enhance signal transmission between base stations and user equipment, reduce interference, and improve communication quality and capacity.

[0183] Radar system: used to accurately detect and locate targets and improve the resolution and anti-interference capabilities of the radar system.

[0184] Sonar system: used for underwater detection and positioning, and beamforming technology is used to improve the detection range and accuracy of the sonar system.

[0185] In this application, the Toeplitz-MR algorithm considers a symmetrical ULA, and uses each array element as a reference array element to obtain the received signals of other array elements, and uses them to reconstruct a Toeplitz matrix to achieve the purpose of signal decorrelation. Compared with the traditional single Toeplitz matrix, this algorithm uses the method of multiple Toeplitz matrix expansion, and increases the number of samples by constructing 2N+1 Toeplitz matrices. This algorithm avoids the performance degradation problem caused by using only part of the information, and by diagonalizing the noise term, the signal decorrelation can be achieved without preprocessing the noise.

[0186] Please refer to Figure 3 As shown, the present application also provides an adaptive signal beamforming device, which is used to implement each step of the above-mentioned adaptive signal beamforming method, and the device includes:

[0187] A matrix module 301 constructs a multiple Toeplitz matrix based on signals received by a ULA array of 2N+1 elements;

[0188] In the field of radar or communication, a uniform linear array (ULA) that is completely symmetrical around a central element is used to receive signals. The array consists of multiple elements and receives M incident signals, including K coherent signals and other independent signals. By deriving mathematical formulas, the vector representation of the array received signal is obtained, which takes into account factors such as the correlation between signals, the array flow matrix, the signal vector, and the noise vector.

[0189] In particular, when the central array element is used as the reference array element, the relationship between the received signals of other array elements and the reference signal is derived. Since the first K signals are coherent, the last MK signals are independent, and the noise and signal are independent of each other, the formula can be simplified to obtain an expression for the received signal matrix.

[0190] In order to deal with the problem of insufficient rank of the covariance matrix caused by the coherent signal, the Toeplitz matrix is ​​constructed using the value of the received signal. In this way, the covariance matrix can be reconstructed into a full rank matrix, thus achieving signal decorrelation.

[0191] A merging module 302, merging the multiple Toeplitz matrices by a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors;

[0192] In order to fully utilize the signal and noise information in the array in the process of improving the signal decorrelation, the previous module is expanded to be repeated with each array element in the array as the reference signal. When a specific array element is used as the reference, the formula for the received signal is rewritten accordingly and converted into a form similar to the original formula, but including the received signal and correlation coefficient based on the current array element.

[0193] Through mathematical transformation, these formulas are further converted into matrix form, which contains matrices composed of array steering vectors and correlation coefficients. In order to deal with the rank-deficient covariance matrix caused by coherent signals, Toeplitz matrices are constructed using the values ​​of the received signals, and these matrices are proven to be full rank, with a rank equal to the number of signals.

[0194] Finally, the final covariance matrix is ​​obtained by summing the squares of the Toeplitz matrices under all reference array elements. This covariance matrix not only utilizes the relevant information of all signals in the spatial array, but also achieves better decorrelation effect through multiple Toeplitz matrix expansion and square sum operations, especially in low signal-to-noise ratio environments. This method does not require preprocessing of noise, which improves the efficiency and accuracy of signal processing.

[0195] A calculation module 303 calculates a steering vector of the signal according to the eigenvalue and the eigenvector;

[0196] After calculating the final covariance matrix, we can obtain the eigenvalues ​​and corresponding eigenvectors arranged in order by performing eigendecomposition on it. These eigenvalues ​​represent the power of each signal, while the eigenvectors are related to the signal's steering vector.

[0197] An optimization module 304 optimizes the steering vector using a preset optimization function to obtain a desired signal steering vector;

[0198] After eigendecomposition, the MUSIC algorithm can be used to estimate the angle of arrival (DOA) of the signal, and the signal steering vector can be calculated through the formula.

[0199] However, in practical applications, the steering vector is susceptible to a variety of error factors and produces mismatches. In order to correct the steering vector, an optimization function is constructed. The function is based on the knowledge of matrix space and uses the principle that the desired signal steering vector and its corresponding eigenvector are in the same subspace and have the largest inner product. The eigenvector obtained by eigendecomposition is substituted into the optimization function to find the eigenvector with the largest inner product with the desired signal steering vector, that is, it is considered that they have the greatest correlation. At this time, the eigenvector and the desired signal steering vector correspond to each other. Since the steering vectors of different signals are orthogonal to each other, this property can be used to further verify and correct the steering vector. Finally, a more accurate desired signal steering vector can be obtained through the corrected steering vector (including the steering vectors from the 0th array element to the Nth array element and the -Nth array element to the 0th array element, obtained by taking conjugate flips and superposition).

[0200] An estimation module 305 estimates noise power using the minimum eigenvalue of the covariance matrix;

[0201] According to the principle of eigendecomposition of the covariance matrix, its eigenvalues ​​and eigenvectors can be used to analyze the characteristics of the signal and noise. Among them, the coherent signal will not affect the noise space, so the noise power can still be estimated by the smallest eigenvalue. This conclusion provides an important basis for signal processing and parameter estimation.

[0202] A relationship module 306, establishing a corresponding relationship between the signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector;

[0203] Since the expansion of multiple Topelitz matrices may lead to changes in the signal space, there is no one-to-one correspondence between the steering vector and its power. Therefore, the following correlation function is defined:

[0204]

[0205] Among them, E s represents the signal subspace, that is, the linear subspace spanned by the first M eigenvectors. k Substitute them into the signal subspace respectively. If the eigenvector corresponds to the steering vector, their correlation function value is the largest. This step can establish a connection between the steering vector and the eigenvector. The eigenvalue and the eigenvector are in one-to-one correspondence, so it is equivalent to a one-to-one correspondence between the steering vector and the power.

[0206] A determination module 307, determining the expected signal power according to the corresponding relationship;

[0207] After all steering vectors are matched to signal powers, the desired signal power is placed in the first column and the decomposition result of formula (31) is rearranged:

[0208]

[0209] Wherein, Σ represents a diagonal matrix composed of all signal powers, B is composed of steering vectors with an array element length of N+1, and the first column is the desired signal steering vector.

[0210] All steering vectors are expanded to 2N+1 using formula (35):

[0211]

[0212] Reconstruct the covariance matrix using the expanded steering vector:

[0213]

[0214] Where A is the array flow matrix composed of the steering vectors in (35). Therefore, the diagonal matrix is:

[0215]

[0216] In formula (37), the non-zero elements in the first column of the diagonal matrix Σ are the desired signal powers. By deleting them, we can obtain a diagonal matrix without the desired signal power.

[0217] A reconstruction module 308, reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the expected signal power;

[0218] The inverse operation of (37) can be used to obtain the covariance matrix of the reconstructed mutually uncorrelated noise (IPNC M), that is:

[0219]

[0220] The weight vector module 309 brings the desired signal steering vector and the covariance matrix of the uncorrelated noise into a Capon beamformer to calculate an optimal weight vector;

[0221] Finally, the desired signal steering vector and the reconstructed IPNCM are brought into the Capon beamformer to obtain the optimal weight vector, which is expressed as follows:

[0222]

[0223] The control module 310 controls the beamforming of the signal according to the optimal weight vector.

[0224] Furthermore, a multiple Toeplitz matrix is ​​constructed based on the signal received by the ULA array of 2N+1 elements, including:

[0225] Taking each array element in the ULA array of the 2N+1 array elements as a reference array element, and using a signal received by each array element as a reference signal;

[0226] The received signals of all array elements are obtained in sequence, and multiple Toeplitz matrices under all reference array elements are repeatedly constructed.

[0227] Furthermore, the multiple Toeplitz matrices are combined through a square sum operation to obtain a covariance matrix, which is expressed as follows:

[0228]

[0229] Among them, R m is a Toeplitz matrix, and N is the array element number.

[0230] Further, calculating the steering vector of the signal according to the eigenvalue and the eigenvector includes:

[0231] The eigenvalues ​​and eigenvectors of the covariance matrix are obtained by eigendecomposition, and the direction of arrival (DOA) of the signal is estimated using the MUSIC algorithm to reconstruct the steering vector.

[0232] Further, determining the expected signal power according to the corresponding relationship includes:

[0233] According to the corresponding relationship, after all steering vectors are matched with signal powers, the expected signal power is placed in the first column, and the expected signal power is obtained by calculating the non-zero elements in the first column of the diagonal matrix.

[0234] Further, reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the expected signal power includes:

[0235] Deleting the desired signal power to obtain a diagonal matrix without the desired signal power;

[0236] An inverse operation is performed on the diagonal matrix to obtain a reconstructed mutually uncorrelated noise covariance matrix.

[0237] Furthermore, it is applied to the control of communication beams or radar beams.

[0238] The present application provides an adaptive signal beamforming device, comprising:

[0239] A memory, used for storing a computer executable program of the above-mentioned adaptive signal beamforming method;

[0240] A processor is used to retrieve the computer executable program from the memory and execute: construct multiple Toeplitz matrices based on signals received by a ULA array of 2N+1 array elements; merge the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors; calculate a steering vector of the signal according to the eigenvalues ​​and the eigenvectors; optimize the steering vector using a preset optimization function to obtain a desired signal steering vector; estimate noise power using the minimum eigenvalue of the covariance matrix; establish a corresponding relationship between signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector; determine the desired signal power according to the corresponding relationship; reconstruct a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the desired signal power; bring the desired signal steering vector and the covariance matrix of the mutually uncorrelated noise into a Capon beamformer to calculate an optimal weight vector; and control the beamforming of the signal according to the optimal weight vector.

[0241] The present application also provides a storage medium, comprising: storing a computer executable program, wherein the computer executable program is used to be called by a processor to execute the steps of the above-mentioned adaptive signal beamforming method.

[0242] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An adaptive signal beamforming method, characterized in that: include: Construct multiple Toeplitz matrices based on the signals received by the ULA array of 2N+1 elements; Combining the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors; Calculating a steering vector of the signal according to the eigenvalue and the eigenvector; Optimizing the steering vector using a preset optimization function to obtain a desired signal steering vector; estimating noise power using the minimum eigenvalue of the covariance matrix; Establishing a corresponding relationship between the signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector; Determine the expected signal power according to the corresponding relationship; Reconstructing a covariance matrix of mutually uncorrelated noises according to the noise power, the corresponding relationship and the expected signal power; Bringing the desired signal steering vector and the covariance matrix of the uncorrelated noise into a Capon beamformer to calculate an optimal weight vector; The beamforming of the signal is controlled according to the optimal weight vector.

2. The adaptive signal beamforming method according to claim 1, characterized in that: A multiple Toeplitz matrix is ​​constructed based on the signal received by the ULA array of 2N+1 elements, including: Taking each array element in the ULA array of the 2N+1 array elements as a reference array element, and the signal as a reference signal; All the reference signals are acquired in sequence, and multiple Toeplitz matrices under all reference array elements are repeatedly constructed.

3. The adaptive signal beamforming method according to claim 1, characterized in that: The multiple Toeplitz matrices are combined through square sum operations to obtain a covariance matrix, which is expressed as follows: Among them, R m is a Toeplitz matrix, and N is the array element number.

4. The adaptive signal beamforming method according to claim 1, characterized in that: Calculating the steering vector of the signal according to the eigenvalue and the eigenvector includes: The eigenvalues ​​and eigenvectors of the covariance matrix are obtained by eigendecomposition, and the direction of arrival (DOA) of the signal is estimated using the MUSIC algorithm to reconstruct the steering vector.

5. The adaptive signal beamforming method according to claim 1, characterized in that: Determining the expected signal power according to the corresponding relationship includes: According to the corresponding relationship, after all steering vectors are matched with signal powers, the expected signal power is placed in the first column, and the expected signal power is obtained by calculating the non-zero elements in the first column of the diagonal matrix.

6. The adaptive signal beamforming method according to claim 5, characterized in that: Reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the expected signal power includes: Deleting the desired signal power to obtain a diagonal matrix without the desired signal power; An inverse operation is performed on the diagonal matrix to obtain a reconstructed mutually uncorrelated noise covariance matrix.

7. The adaptive signal beamforming method according to any one of claims 1 to 6, characterized in that: The method is applied to the control of a communication beam or a radar beam.

8. An adaptive signal beamforming device, characterized in that: include: The matrix module constructs a multi-Toeplitz matrix based on the signals received by the ULA array of 2N+1 elements; A merging module, merging the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors; A calculation module, which calculates a steering vector of the signal according to the eigenvalue and the eigenvector; An optimization module, which optimizes the steering vector using a preset optimization function to obtain a desired signal steering vector; An estimation module, using the minimum eigenvalue of the covariance matrix to estimate noise power; A relationship module, which establishes a corresponding relationship between the signal power and the feature vector according to the correlation between the feature vector and the desired signal steering vector; A determination module, which determines the expected signal power according to the corresponding relationship; A reconstruction module, reconstructing a covariance matrix of mutually uncorrelated noises according to the noise power, the corresponding relationship and the expected signal power; A weight vector module, which brings the desired signal steering vector and the covariance matrix of the uncorrelated noise into a Capon beamformer to calculate an optimal weight vector; A control module controls the beamforming of the signal according to the optimal weight vector.

9. The adaptive signal beamforming device according to claim 8, characterized in that: A multiple Toeplitz matrix is ​​constructed based on the signal received by the ULA array of 2N+1 elements, including: Taking each array element in the ULA array of the 2N+1 array elements as a reference array element, and using a signal received by each array element as a reference signal; All the reference signals are acquired in sequence, and multiple Toeplitz matrices under all reference array elements are repeatedly constructed.

10. The adaptive signal beamforming device according to claim 8, characterized in that: The multiple Toeplitz matrices are combined through square sum operations to obtain a covariance matrix, which is expressed as follows: Among them, R m is a Toeplitz matrix, and N is the array element number.

11. The adaptive signal beamforming device according to claim 8, characterized in that: Calculating the steering vector of the signal according to the eigenvalue and the eigenvector includes: The eigenvalues ​​and eigenvectors of the covariance matrix are obtained by eigendecomposition, and the direction of arrival (DOA) of the signal is estimated using the MUSIC algorithm to reconstruct the steering vector.

12. The adaptive signal beamforming device according to claim 8, characterized in that: Determining the expected signal power according to the corresponding relationship includes: According to the corresponding relationship, after all steering vectors are matched with signal powers, the expected signal power is placed in the first column, and the expected signal power is obtained by calculating the non-zero elements in the first column of the diagonal matrix.

13. The adaptive signal beamforming device according to claim 12, characterized in that: Reconstructing a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the expected signal power includes: Deleting the desired signal power to obtain a diagonal matrix without the desired signal power; An inverse operation is performed on the diagonal matrix to obtain a reconstructed mutually uncorrelated noise covariance matrix.

14. The adaptive signal beamforming device according to any one of claims 8 to 13, characterized in that: Used in the control of communication beams or radar beams.

15. An adaptive signal beamforming device, characterized in that: include: A memory for storing a computer executable program of an adaptive signal beamforming method according to any one of claims 1 to 7; A processor is used to retrieve the computer executable program from the memory and execute: construct multiple Toeplitz matrices based on signals received by a ULA array of 2N+1 array elements; merge the multiple Toeplitz matrices through a square sum operation to obtain a covariance matrix, wherein the covariance matrix includes eigenvalues ​​and eigenvectors; calculate a steering vector of the signal according to the eigenvalues ​​and the eigenvectors; optimize the steering vector using a preset optimization function to obtain a desired signal steering vector; estimate noise power using the minimum eigenvalue of the covariance matrix; establish a corresponding relationship between signal power and the eigenvector according to the correlation between the eigenvector and the desired signal steering vector; determine the desired signal power according to the corresponding relationship; reconstruct a covariance matrix of mutually uncorrelated noise according to the noise power, the corresponding relationship and the desired signal power; bring the desired signal steering vector and the covariance matrix of the mutually uncorrelated noise into a Capon beamformer to calculate an optimal weight vector; and control the beamforming of the signal according to the optimal weight vector.

16. A storage medium, characterized in that: include: A computer executable program is stored, and the computer executable program is used to be called by a processor to execute the steps of an adaptive signal beamforming method as described in any one of claims 1 to 7.

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