Robust beamforming method based on interference-plus-noise covariance matrix reconstruction

Abstract

This invention discloses a robust beamforming method based on interference plus noise covariance matrix reconstruction. The method includes: calculating the DOA (Directivity of Analysis) by using a priori known reference sequence of the desired signal and the received signal to calculate the cross-correlation vector, effectively suppressing unrelated interference and noise; selecting the main lobe sector boundary containing the desired signal and numerically integrating the Capon spatial power spectrum of the received signal within the spatial angle interval where pure interference and noise are located to reconstruct a pure interference plus noise covariance matrix, eliminating the influence of the desired signal component; and calculating the optimal weighting vector based on the cross-correlation vector and the reconstructed interference plus noise covariance matrix. This method can sense the spatial interference distribution and form deeper nulls in the interference direction, significantly improving the output signal-to-interference-plus-noise ratio and beamforming robustness of the array system.

Description

Technical Field

[0001] This invention relates to the field of array antenna signal processing technology, and more specifically to a robust beamforming method based on interference plus noise covariance matrix reconstruction. Background Technology

[0002] Array signal processing, as a key core technology in modern radar, sonar, and wireless communication systems, plays an irreplaceable role in spatial filtering, target detection, and interference suppression. Among them, adaptive beamforming technology, by adjusting the weighting coefficients of each element of the array antenna, can enhance the desired signal while creating a minimum value in the direction of interference, thereby significantly improving the output signal-to-interference-plus-noise ratio of the system.

[0003] One of the most classic adaptive beamforming algorithms is the minimum variance distortionless response beamformer. However, in practical engineering applications, due to factors such as array manifold errors and mismatch in the estimation of the desired signal's direction, the sampling covariance matrix calculated from the actual received snapshot data often contains components of the desired signal. This model mismatch causes the adaptive beamformer to mistakenly identify the desired signal as interference and suppress it, leading to a sharp deterioration in system performance.

[0004] To overcome these shortcomings, researchers have proposed various robust adaptive beamforming algorithms, such as the diagonal loading method and the feature space method. In recent years, algorithms based on interference plus noise covariance matrix reconstruction have attracted much attention. These methods typically utilize the Capon spatial power spectrum for spatial integration to reconstruct a clean interference plus noise covariance matrix, thereby fundamentally avoiding the negative impact of the desired signal on weight calculation.

[0005] However, existing robust beamforming methods based on covariance matrix reconstruction still have significant limitations: First, traditional reconstruction methods are highly dependent on the precise division of the angular interval where the desired signal is located. Most existing technologies rely solely on simple conventional angle measurement algorithms to obtain the DOA of the desired signal, without fully exploring and utilizing the prior statistical information of the signal's spatial distribution. In complex electromagnetic environments with low signal-to-noise ratios or strong interference, conventional angle measurement methods often exhibit significant DOA estimation bias. Second, once the DOA estimation of the desired signal is severely biased, the set removal interval will shift during covariance matrix reconstruction. This not only leads to some interference energy not being effectively integrated, but more critically, it causes the energy of the desired signal to be incorrectly mixed into the reconstructed interference-plus-noise covariance matrix, ultimately resulting in distortion of the calculated beamforming weight vector, making it impossible to form accurate deep nulls in the interference direction, and significantly reducing the robustness of the system. Summary of the Invention

[0006] The present invention proposes a robust beamforming method based on interference plus noise covariance matrix reconstruction to solve the problems mentioned above in the background.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The robust beamforming method based on interference plus noise covariance matrix reconstruction of the present invention includes the following steps: S1. Receive array received signals with a specified number of snapshots and estimate the sampling covariance matrix of the array received signals; S2. Based on the array received signal, estimate the cross-correlation vector using the prior known sequence of the desired signal to obtain the estimated value of the desired signal DOA; S3. Reconstruct the interference plus noise covariance matrix based on the estimated value of the desired signal DOA; S4. Calculate the optimal weighting vector for robust beamforming using the cross-correlation vector of the received signal and the known desired signal, and the reconstructed interference plus noise covariance matrix; S5. Use the optimal weighting vector of robust beamforming to perform weighted filtering on the snapshot data received by the array to obtain the beamforming signal.

[0008] Preferably, S1 includes: Receive array signals for a specified number of snapshots; Estimate the sampling covariance matrix of the received signal:

[0009] in, For the number of snapshots, , for The conjugate transpose of; The array received signal is represented as:

[0010] in, and Representing the desired signal and the first The steering vector of the interference signal, and Indicates the expected signal and the first The true direction of arrival of the interference signal. and Let these represent the complex envelopes of the desired signal and the interference signal, respectively. The number of interference sources It is additive zero-mean Gaussian white noise.

[0011] Preferably, S2 includes: Estimate the cross-correlation vector using the prior known sequence of the desired signal:

[0012] in, Indicates taking the conjugate; Estimate the direction of arrival of the desired signal:

[0013] in, This represents the possible set of directions of incoming waves.

[0014] Preferably, S3 includes: Based on the estimated value of the desired signal DOA, a sector boundary width containing the desired signal is set. ; Based on the influence of the desired signal on the interference-noise covariance matrix, the two angular sub-intervals containing the interference signal are defined as follows: and ; In the aforementioned interval Internally, based on the Capon spatial power spectrum of the received signal, numerical integration is performed to reconstruct the interference plus noise covariance matrix:

[0015] in, for The conjugate transpose of .

[0016] Preferably, S4 includes: The optimal weighting vector for robust beamforming is calculated using the cross-correlation vector of the received signal and the known desired signal, and the reconstructed interference plus noise covariance matrix. .

[0017] Preferably, the beamforming signal is as follows:

[0018] in, for The conjugate transpose of .

[0019] As can be seen from the above technical solution, this invention provides a robust beamforming method based on interference plus noise covariance matrix reconstruction. Compared with the prior art, this invention has the following advantages: By using the prior known reference sequence of the desired signal and the received signal to calculate the cross-correlation vector for DOA estimation, unrelated interference and noise are effectively suppressed; by selecting the main lobe sector boundary containing the desired signal, the Capon spatial power spectrum of the received signal is numerically integrated within the spatial angle interval where pure interference and noise are located, a pure interference plus noise covariance matrix is ​​reconstructed, eliminating the influence of the desired signal component; based on the cross-correlation vector and the reconstructed interference plus noise covariance matrix, the optimal weighting vector is calculated. This method can sense the spatial interference distribution and form deeper nulls in the interference direction, significantly improving the output signal-to-interference-plus-noise ratio and beamforming robustness of the array system. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the robust beamforming method based on interference plus noise covariance matrix reconstruction according to the present invention.

[0021] Figure 2 This is a comparison diagram of the radiation patterns of the present invention and the MMSE beamforming algorithm.

[0022] Figure 3 This is a comparison chart showing the output signal-to-interference-plus-noise ratio (SNR) of the present invention and the MMSE beamforming algorithm as a function of the input SNR. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0024] like Figure 1 As shown, the robust beamforming method based on interference plus noise covariance matrix reconstruction in this embodiment includes the following steps: S1. Receive array received signals with a specified number of snapshots and estimate the sampling covariance matrix of the array received signals; S2. Based on the array received signal, estimate the cross-correlation vector using the prior known sequence of the desired signal to obtain the estimated value of the desired signal DOA; S3. Reconstruct the interference plus noise covariance matrix based on the estimated value of the desired signal DOA; S4. Calculate the optimal weighting vector for robust beamforming using the cross-correlation vector of the received signal and the known desired signal, and the reconstructed interference plus noise covariance matrix; S5. Use the optimal weighting vector of robust beamforming to perform weighted filtering on the snapshot data received by the array to obtain the beamforming signal.

[0025] Furthermore, S1 includes: Receive array signals for a specified number of snapshots; Estimate the sampling covariance matrix of the received signal:

[0026] in, For the number of snapshots, , for The conjugate transpose of; The array received signal is represented as:

[0027] in, and Representing the desired signal and the first The steering vector of the interference signal, and Indicates the expected signal and the first The true direction of arrival of the interference signal. and Let these represent the complex envelopes of the desired signal and the interference signal, respectively. The number of interference sources It is additive zero-mean Gaussian white noise.

[0028] Furthermore, S2 includes: Estimate the cross-correlation vector using the prior known sequence of the desired signal:

[0029] in, Indicates taking the conjugate; Estimate the direction of arrival of the desired signal:

[0030] in, This represents the possible set of directions of incoming waves.

[0031] Furthermore, S3 includes: Based on the estimated value of the desired signal DOA, a sector boundary width containing the desired signal is set. ; Based on the influence of the desired signal on the interference-noise covariance matrix, the two angular sub-intervals containing the interference signal are defined as follows: and ; In the interval Internally, based on the Capon spatial power spectrum of the received signal, numerical integration is performed to reconstruct the interference plus noise covariance matrix:

[0032] in, for The conjugate transpose of .

[0033] Furthermore, S4 includes: The optimal weighting vector for robust beamforming is calculated using the cross-correlation vector of the received signal and the known desired signal, and the reconstructed interference plus noise covariance matrix. .

[0034] Furthermore, the beamforming signal is as follows:

[0035] in, for The conjugate transpose of .

[0036] To verify the robust beamforming method proposed in this invention based on the reconstruction of cross-correlation vectors and interference plus noise covariance matrix, two sets of experiments were conducted and analyzed: array beamforming pattern analysis and output SINR performance analysis under different input SNR conditions.

[0037] Consider a uniform linear array with 4 elements spaced 0.5 wavelengths apart. Three independent, non-interfering signal sources propagate to the array from far-field azimuths of -5°, -35°, and 40°. The first direction represents the desired signal, while the other two directions represent interference signals. In this experiment, the desired signal is an independently and identically distributed BPSK modulated signal, and the interference signal is a broadband complex Gaussian signal. The interference-to-noise ratio (INR) at the array receiver is 20 dB, the background noise is Gaussian white noise, and the snapshot number is 100.

[0038] The specific method for reconstructing the interference plus noise covariance matrix is ​​as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Mapping to the sinusoidal space, we obtain the corresponding upper and lower limits of the sinusoidal domain integral: and , and The first The first and last angles of each angular subinterval; the order of the Gauss-Chebyshev integral is set to . First, within the standard range. above generated A standard Chebyshev node:

[0039] Subsequently, the standard nodes are mapped to the target sine sub-interval. Obtain the actual points node:

[0040] Combining Chebyshev orthogonal weight properties and interval mapping, calculate the numerical integral weights corresponding to each node:

[0041] Using the obtained integration nodes and weights, and combining the Capon spatial spectrum principle, the interference plus noise covariance matrix is ​​reconstructed through discrete weighted summation.

[0042] in, For nodes in sinusoidal space The guide vector at that location, .

[0043] The performance of the method of this invention will be analyzed below through simulation results. The method of this invention will be compared with the beamforming algorithm of MMSE.

[0044] Figure 2 This image shows a comparison of the beam patterns of the method described in this invention with those of the traditional MMSE method. As can be seen from the image, the method of this invention not only accurately estimates the angle of arrival of the desired signal and precisely aligns the main lobe peak with the target, but more importantly, it creates a deeper null in the direction of the interference source compared to the traditional MMSE method. Furthermore, this method effectively reduces the overall spatial sidelobe level. This fully demonstrates that after removing the desired signal contamination, the present invention can more accurately perceive the interference distribution, thereby achieving stronger interference suppression and noise filtering capabilities.

[0045] Figure 3 The figure shows the output SINR comparison curves of the proposed method and the traditional MMSE method under different input signal-to-noise ratios. As can be seen from the figure, when SNR > 5, the proposed method can consistently provide approximately 3 higher SINR than the traditional MMSE method. The output SINR is 4dB. More importantly, as the input signal-to-noise ratio increases, the output SINR of the method of this invention maintains an excellent linear increasing trend, effectively overcoming the problems of weight calculation deviation and performance limitation caused by excessively large expected signal components in traditional methods under high signal-to-noise ratio environments.

[0046] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk), etc.

[0047] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0048] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0049] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robust beamforming method based on interference plus noise covariance matrix reconstruction, characterized in that, Includes the following steps: S1. Receive array received signals with a specified number of snapshots and estimate the sampling covariance matrix of the array received signals; S2. Based on the array received signal, estimate the cross-correlation vector using the prior known sequence of the desired signal to obtain the estimated value of the desired signal DOA; S3. Reconstruct the interference plus noise covariance matrix based on the estimated value of the desired signal DOA; S4. Calculate the optimal weighting vector for robust beamforming using the cross-correlation vector of the received signal and the known desired signal, and the reconstructed interference plus noise covariance matrix; S5. Use the optimal weighting vector of robust beamforming to perform weighted filtering on the snapshot data received by the array to obtain the beamforming signal.

2. The robust beamforming method based on interference plus noise covariance matrix reconstruction according to claim 1, characterized in that: S1 includes: Receive array signals for a specified number of snapshots; Estimate the sampling covariance matrix of the received signal: in, For the number of snapshots, , for The conjugate transpose of; The array received signal is represented as: in, and Representing the desired signal and the first The steering vector of the interference signal, and Indicates the expected signal and the first The true direction of arrival of the interference signal. and Let these represent the complex envelopes of the desired signal and the interference signal, respectively. The number of interference sources It is additive zero-mean Gaussian white noise.

3. The robust beamforming method based on interference plus noise covariance matrix reconstruction according to claim 2, characterized in that: S2 includes: Estimate the cross-correlation vector using the prior known sequence of the desired signal: in, Indicates taking the conjugate; Estimate the direction of arrival of the desired signal: in, This represents the possible set of directions of incoming waves.

4. The robust beamforming method based on interference plus noise covariance matrix reconstruction according to claim 3, characterized in that: S3 includes: Based on the estimated value of the desired signal DOA, a sector boundary width containing the desired signal is set. ; Based on the influence of the desired signal on the interference-noise covariance matrix, the two angular sub-intervals containing the interference signal are defined as follows: and ; In the aforementioned interval Internally, based on the Capon spatial power spectrum of the received signal, numerical integration is performed to reconstruct the interference plus noise covariance matrix: in, for The conjugate transpose of .

5. The robust beamforming method based on interference plus noise covariance matrix reconstruction according to claim 4, characterized in that: S4 includes: The optimal weighting vector for robust beamforming is calculated using the cross-correlation vector of the received signal and the known desired signal, and the reconstructed interference plus noise covariance matrix. 。 6. The robust beamforming method based on interference plus noise covariance matrix reconstruction according to claim 5, characterized in that: The beamforming signal is as follows: in, for The conjugate transpose of .

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