Method, apparatus and device for analyzing performance of adaptive beamformer in interference power

Abstract

The application relates to the technical field of array signal processing, in particular to a method, device and equipment for analyzing the performance of an adaptive beamformer under interference power. The method comprises the following steps: constructing a received signal vector in a preset array receiving scenario, calculating a covariance matrix of the received signal and separating to obtain a joint covariance matrix of interference and noise; based on a minimum variance distortionless response criterion, determining beamforming weights by using the joint covariance matrix, and calculating a quantitative relationship between interference power and null depth according to the beamforming weights by using a matrix inversion and subspace decomposition method; calculating the curvature of a beam pattern at a null point by using derivatives of the beamforming weights and an interference steering vector, obtaining a qualitative relationship between the interference power and the null width according to the correlation between the curvature and the null width; and outputting a performance analysis result according to the quantitative and qualitative relationships. The method can improve the interference suppression reliability of the adaptive beamformer in a complex electromagnetic environment.

Description

Technical Field

[0001] This application relates to the field of array signal processing technology, and in particular to a method, apparatus and device for analyzing the performance of an adaptive beamformer by interference power. Background Technology

[0002] With the technological advancements in wireless communication and array signal processing, adaptive beamforming has gradually become a core technology in applications such as radar, communication base stations, and satellite receiving systems. This technology dynamically adjusts the weighting coefficients of array elements to suppress interference signals while focusing the desired signal, significantly improving the signal-to-noise ratio and interference suppression capabilities. It is currently widely used in signal processing scenarios under complex electromagnetic environments. Traditional adaptive beamforming performance analysis methods often focus on optimizing algorithm complexity or increasing the desired signal gain, rarely addressing the crucial variable of interference power and systematically analyzing its impact on the beamformer's core interference suppression metrics (null depth and null width).

[0003] However, current performance analysis methods have significant shortcomings: on the one hand, they cannot clearly define the quantitative relationship between interference power and null depth, making it difficult to accurately determine the magnitude of change in interference suppression effect when interference is enhanced; on the other hand, they do not reveal the trend of interference power's influence on null width, leading to either an overemphasis on deep nulls at the expense of directional shift sensitivity caused by excessively narrow nulls in actual designs, or a sacrifice of interference suppression capability due to conservative design, ultimately resulting in poor stability and unreasonable resource allocation of beamformers in high dynamic interference scenarios. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, and device for analyzing the effect of interference power on the performance of adaptive beamformers, addressing the aforementioned technical problems.

[0005] A method for analyzing the effect of interference power on the performance of an adaptive beamformer, the method comprising: Based on the pre-set array receiving scenario, a received signal vector containing the desired signal, interference signal and noise is constructed, the covariance matrix of the received signal is calculated, and the joint covariance matrix of interference and noise is obtained. Based on the minimum variance distortionless response criterion, the beamforming weights are determined using the joint covariance matrix of the interference and noise. Then, using matrix inversion and subspace decomposition methods, the quantitative relationship between interference power and null depth is calculated based on the beamforming weights. Using the derivatives of the beamforming weights and the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion. Based on the correlation between the curvature and the null width, a qualitative relationship between the interference power and the null width is obtained. Based on the quantitative and qualitative relationships described above, the analysis results of the impact of output interference power on the performance of the adaptive beamformer are presented.

[0006] An apparatus for analyzing the performance of an adaptive beamformer by interference power, the apparatus comprising: The signal modeling module is used to construct a received signal vector containing the desired signal, interference signal and noise based on the pre-set array receiving scenario, calculate the covariance matrix of the received signal and separate the joint covariance matrix of interference and noise. The quantitative analysis module is used to determine the beamforming weights based on the minimum variance distortionless response criterion using the joint covariance matrix of the interference and noise, and to calculate the quantitative relationship between the interference power and the null depth using matrix inversion and subspace decomposition methods based on the beamforming weights. The qualitative analysis module is used to calculate the curvature of the beam pattern at the null using the derivative of the beamforming weight and the interference steering vector through Taylor expansion, and to obtain the qualitative relationship between the interference power and the null width based on the correlation between the curvature and the null width. The results output module is used to output the analysis results of the interference power on the performance of the adaptive beamformer based on the quantitative and qualitative relationships.

[0007] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps: Based on the pre-set array receiving scenario, a received signal vector containing the desired signal, interference signal and noise is constructed, the covariance matrix of the received signal is calculated, and the joint covariance matrix of interference and noise is obtained. Based on the minimum variance distortionless response criterion, the beamforming weights are determined using the joint covariance matrix of the interference and noise. Then, using matrix inversion and subspace decomposition methods, the quantitative relationship between interference power and null depth is calculated based on the beamforming weights. Using the derivatives of the beamforming weights and the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion. Based on the correlation between the curvature and the null width, a qualitative relationship between the interference power and the null width is obtained. Based on the quantitative and qualitative relationships described above, the analysis results of the impact of output interference power on the performance of the adaptive beamformer are presented.

[0008] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Based on the pre-set array receiving scenario, a received signal vector containing the desired signal, interference signal and noise is constructed, the covariance matrix of the received signal is calculated, and the joint covariance matrix of interference and noise is obtained. Based on the minimum variance distortionless response criterion, the beamforming weights are determined using the joint covariance matrix of the interference and noise. Then, using matrix inversion and subspace decomposition methods, the quantitative relationship between interference power and null depth is calculated based on the beamforming weights. Using the derivatives of the beamforming weights and the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion. Based on the correlation between the curvature and the null width, a qualitative relationship between the interference power and the null width is obtained. Based on the quantitative and qualitative relationships described above, the analysis results of the impact of output interference power on the performance of the adaptive beamformer are presented.

[0009] The aforementioned method, apparatus, and equipment for analyzing the performance of adaptive beamformers by interfering power, through pre-setting the array receiving scenario, constructs a received signal vector containing the desired signal, interfering signal, and noise, calculates and separates the joint covariance matrix of interference and noise, providing an accurate signal statistical basis for subsequent performance analysis. Beamforming weights are determined based on the minimum variance distortion-free response criterion. Combining matrix inversion and subspace decomposition methods, a quantitative relationship between interfering power and null depth can be established, solving the problem that traditional methods cannot quantify the impact of interference enhancement on beam interference suppression. Using the weights and the derivative of the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion and correlated with the null width, clarifying the qualitative relationship between interfering power and null width. This avoids the sensitivity problem of directional shift caused by neglecting changes in null width. Finally, by outputting the analysis results, a quantitative basis is provided for the performance evaluation of adaptive beamformers, enabling accurate analysis based on the correlation between interfering power and null indicators, effectively improving the reliability of interference suppression of adaptive beamformers in complex electromagnetic environments. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the method for analyzing the effect of interference power on the performance of an adaptive beamformer in one embodiment. Figure 2 This is a schematic diagram of simulation results in one embodiment, wherein, Figure 2 (a) is a schematic diagram of the simulation results when the interference signal has a noise ratio of 15dB. Figure 2 (b) is a schematic diagram of the simulation results when the interference signal interference-to-noise ratio is 20dB. Figure 2 (c) is a schematic diagram of the simulation results when the interference signal interference-to-noise ratio is 30dB. Figure 2 (d) is a schematic diagram of the simulation results when the interference signal interference-to-noise ratio is 40dB; Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0012] In one embodiment, such as Figure 1 As shown, a method for analyzing the effect of interference power on the performance of an adaptive beamformer is provided, including the following steps: Step 102: Based on the pre-set array receiving scenario, construct a received signal vector containing the desired signal, interference signal and noise, calculate the covariance matrix of the received signal and separate the joint covariance matrix of interference and noise.

[0013] Array reception scenarios refer to signal reception environments based on array antennas (such as uniform linear arrays), including parameters such as the number of array elements and their spacing; the received signal vector is the collection of signals received by each array element, representing the spatial signal distribution in vector form; the covariance matrix is ​​a matrix describing the statistical correlation between the components of the received signal, reflecting signal energy and interference characteristics; the joint covariance matrix of interference and noise is a matrix containing only the statistical characteristics of interference signals and noise after removing the desired signal component from the total covariance matrix, denoted as [equation missing]. .

[0014] It is understandable that by constructing a signal model and separating the key matrix, interference from the desired signal can be eliminated for subsequent analysis, ensuring that only the impact of interference power on beam performance is focused, thus providing an accurate data analysis foundation for the entire method.

[0015] Step 104: Based on the minimum variance distortionless response criterion, the beamforming weights are determined using the joint covariance matrix of interference and noise. The matrix inversion and subspace decomposition methods are used to calculate the quantitative relationship between interference power and null depth based on the beamforming weights.

[0016] The Minimum Variance Distortionless Response (MVDR) criterion is an optimization criterion that minimizes the output power of the beamformer while ensuring distortion-free signal in the desired direction. Beamforming weights are a coefficient vector that weights the signals of each array element. The shape of the beam pattern is directly determined. Matrix inversion simplifies the covariance matrix inversion operation using the Sherman-Morrison lemma. Subspace decomposition is achieved through eigenvalue decomposition. It is separated into an interference subspace (containing strong interference information) and a noise subspace (containing noise information). The null depth is the amount of attenuation of the beam gain in the interference direction relative to the gain in the desired direction (in decibels), which characterizes the strength of interference suppression capability.

[0017] It is understandable that by establishing a quantitative correspondence between the change in interference power and the change in null depth, it is possible to quantify the extent to which interference enhancement affects the beam interference suppression effect.

[0018] Step 106: Using the derivatives of the beamforming weights and the interference steering vector, calculate the curvature of the beam pattern at the null using Taylor expansion. Based on the correlation between curvature and null width, obtain the qualitative relationship between interference power and null width.

[0019] Interference steering vector It is a vector describing the array's response to an incoming interference signal from a specific direction, and is directly related to the interference angle. The derivative of the interference steering vector is the first derivative of the steering vector with respect to the angle of arrival. The rate of change of the guide vector when the angle shifts is reflected. Taylor expansion is a mathematical method that approximates the beam pattern as a quadratic function at the interference angle. It is used to extract the curvature at the null. The curvature is the coefficient of the squared angle term in the quadratic expansion, which reflects the rate of increase of beam gain with angle shift. The null width is the angular range in which the beam gain near the interference direction increases from the minimum value to a preset threshold, which characterizes the tolerance of the null to angle shift.

[0020] It is understandable that by analyzing the qualitative trends between interference power and null curvature, as well as between null curvature and null width, the problem of null failure due to angular offset in high interference scenarios caused by neglecting the correlation between null width and interference power in traditional designs has been solved, providing theoretical support for balancing interference suppression capability and directional adaptability.

[0021] Step 108: Based on the quantitative and qualitative relationships, output the analysis results of the impact of interference power on the performance of the adaptive beamformer.

[0022] Analysis results include: increasing interference power This will cause the beam pattern to change in the direction of the interference. Gain Decrease, i.e., deepen the null trap; for every increase in interference power The depth of the zero trap approximately increases As the interference power increases, the width of the null trap will become narrower.

[0023] It is worth noting that, based on the above conclusions, the greater the interference power, the deeper the null depth, indicating that increasing the interference power can effectively suppress interference. However, the greater the interference power, the narrower the null width. If the estimated interference direction deviates significantly from the actual interference direction, the formed null cannot be effectively aligned with the interference direction. Furthermore, this also has adverse effects in high-dynamic scenarios, where effective null generation is highly sensitive to the accuracy of the interference direction. Therefore, the method of this invention provides a quantitative analysis basis for balancing interference suppression effectiveness and stability in complex interference scenarios, lays a theoretical foundation for the performance evaluation and subsequent targeted optimization of beamformers, and significantly improves their interference suppression reliability in complex electromagnetic environments.

[0024] In the aforementioned method for analyzing the performance of adaptive beamformers by interference power, a received signal vector containing the desired signal, interference signal, and noise is constructed by pre-setting the array receiving scenario. The joint covariance matrix of interference and noise is calculated and separated, providing an accurate signal statistical basis for subsequent performance analysis. The beamforming weights are determined based on the minimum variance distortionless response criterion. By combining matrix inversion and subspace decomposition methods, a quantitative relationship between interference power and null depth can be established, solving the problem that traditional methods cannot quantify the impact of interference enhancement on beam interference suppression. Using the derivatives of the weights and the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion and correlated with the null width. The qualitative relationship between interference power and null width can be clarified, avoiding the directional shift sensitivity problem caused by ignoring changes in null width. Finally, by outputting the analysis results, a quantitative basis is provided for the performance evaluation of adaptive beamformers, enabling accurate analysis based on the correlation between interference power and null index, effectively improving the reliability of interference suppression of adaptive beamformers in complex electromagnetic environments.

[0025] In one embodiment, the array receiving scenario includes a uniform linear array scenario, wherein the uniform linear array contains N array elements, and the spacing between adjacent array elements is less than half the wavelength of the received signal, where N is a positive integer greater than 1.

[0026] In one embodiment, the desired signal is a far-field narrowband signal, the interference signal is a K-channel far-field narrowband interference signal, and the noise is additive white Gaussian noise. The desired signal, the interference signal, and the noise are uncorrelated, where K is a positive integer.

[0027] Specifically, a by A uniform linear array (ULA) composed of 100 elements receives signals from... A far-field narrowband interference signal and a desired signal. At time, the signal vector received by the array It can be represented as: ; in Let the complex envelope of the desired signal have power . , This is the steering vector for the desired signal. Representing the The complex envelope of the interference signal, with power of , For the first The steering vector of an interference signal. Let be an additive white Gaussian noise (AWGN) vector, and its covariance matrix be... , It is noise power. yes A 3D identity matrix. For ease of representation, the interference signal is written in matrix form, i.e.: ; in It is the interference steering vector matrix. Interference signal vector.

[0028] Furthermore, since the desired signal, interference, and noise are all uncorrelated, the covariance matrix can be expressed as: ; in This is the diagonal matrix of interference power.

[0029] In one embodiment, the matrix inversion and subspace decomposition method is used to calculate the quantitative relationship between interference power and null depth based on beamforming weights. This includes: decomposing the joint covariance matrix of interference and noise into the sum of the residual covariance matrix and the target interference term; the residual covariance matrix contains other interference and noise components besides the interference to be analyzed, and the target interference term is the product of the power of the interference to be analyzed and the outer product of the corresponding steering vector; calculating the beamforming weights using the matrix inversion lemma based on the minimum variance distortionless response criterion and the sum of the residual covariance matrix and the target interference term; the expression for the beamforming weights includes the power parameter of the interference to be analyzed; substituting the beamforming weights into the beam gain formula in the interference direction to obtain the functional relationship between beam gain and the power of the interference to be analyzed, and determining the attenuation trend of the gain in the interference direction as the interference power increases based on the functional relationship. The qualitative relationship between null depth and interference power is obtained by using the potential of the function. The interference subspace and noise subspace in the joint covariance matrix are separated using a subspace decomposition method. Based on the functional relationship and the qualitative relationship between null depth and interference power, a quantitative relationship between null depth and interference power is obtained. The attenuation trend of interference direction gain as interference power increases is determined based on the functional relationship. The qualitative relationship between null depth and interference power includes: performing a magnitude analysis on the functional relationship between beam gain and the interference power to be analyzed, determining the growth magnitude of the numerator and denominator terms in the functional relationship; where the numerator term is a constant term independent of the interference power to be analyzed, and the denominator term is a term that increases quadratically with increasing interference power; based on the difference in magnitude between the numerator and denominator terms, a qualitative relationship is obtained that the null depth monotonically increases with increasing interference power.

[0030] Specifically, according to the MVDR criterion, in the interference plus noise covariance matrix and Given the weight vector for: ; in It is a normalization constant. To simplify the analysis, It can be written as an explicit dependency The form is: ; in Defined as the residual covariance matrix, which contains all variables except the first one. All interference signal components and noise other than the individual interferences. Using the matrix inversion lemma and the Sherman-Morrison formula, we get: ; Beamformer in any direction power gain Defined as: ; Then the radiation pattern is in the direction of interference. The response is: ; in If it is a scalar, then the pattern gain... for: ; The denominator can be observed It is a positive constant, and as... The increase in increases dramatically. The constant term in the molecule... Completely independent It is also a fixed positive number. When hour, It will approach a point in A matrix with projection zeros in a direction is still a bounded matrix. Therefore It will approach a non-zero constant, which means It also approaches a finite constant, and its growth is bounded. Furthermore, due to the growth rate of the denominator (on the order of magnitude...), Much faster than molecules (on the order of magnitude) ),when When it increases, the entire fraction The value of must necessarily decrease strictly.

[0031] In one embodiment, a subspace decomposition method is used to separate the interference subspace and noise subspace in the joint covariance matrix. Based on the functional relationship and the qualitative relationship between null depth and interference power, the quantitative relationship between null depth and interference power is obtained, including: performing eigenvalue decomposition on the joint covariance matrix of interference and noise to obtain eigenvalues ​​and corresponding eigenvectors; assigning eigenvectors with eigenvalues ​​greater than a preset noise power threshold to the interference subspace; determining the synchronous scaling relationship between the interference power to be analyzed and the corresponding eigenvalues ​​in the interference subspace; using the synchronous scaling relationship to analyze the scaling law of interference directional gain with interference power in the functional relationship; using the scaling law to convert the interference directional gain into an interference power decibel value; and based on the linear correspondence between the change in interference power decibel value and the change in null depth decibel value, obtaining the quantitative relationship when the interference power to be analyzed increases. At decibel level, the zero-depression depth increases. Quantitative relationship of decibels.

[0032] Specifically, subspace theory is used to... Perform eigenvalue decomposition, that is: ; in It is the eigenvector matrix. It is an eigenvalue diagonal matrix. The inverse is: ; in It is an eigenvalue. It is the corresponding feature vector. These represent the eigenvalues ​​of the interference signal subspace. When the interference power is high... It is a very small number. It is a relatively large number. Because of the vector... If it is almost entirely located within the interference subspace, then when Acting on Then: ; Considering all interference power Simultaneously increase a factor Its eigenvalues Become , The size will be approximately the same as Proportional. When the interference power is large enough, that is... , The amplitude is approximately reduced to the original value. .because Proportional to ,and Not within the interference subspace, therefore The changes are quite complex, but its projection in the interference direction decreases sharply, and the pattern gain... The size will be approximately the same as Proportional. In logarithmic... The following linear relationship can be obtained from the domain: ; in It is a with Irrelevant constants.

[0033] In one embodiment, the curvature of the beam pattern at the null is calculated using the derivatives of the beamforming weights and the interference steering vector through Taylor expansion. Based on the correlation between curvature and null width, a qualitative relationship between interference power and null width is obtained, including: calculating the first derivative of the interference steering vector with respect to the angle of arrival based on the array element distribution; the derivative reflects the rate of change of the steering vector with angular offset; performing a second-order Taylor expansion of the beam pattern at the interference angle of arrival to obtain an expression for the curvature coefficient related to the square term of the angular offset in the expansion; substituting the beamforming weights and the first derivative into the curvature coefficient expression to obtain the correlation between curvature and beamforming weights; and obtaining a qualitative relationship that the null width gradually narrows as the interference power increases based on the correlation between curvature and beamforming weights.

[0034] Specifically, the width of the null is measured by analyzing the curvature of the beam pattern in the direction of interference. From the direction of the interference Performing a Taylor expansion in the vicinity, and ignoring the influence of higher-order terms, the second-order Taylor expansion is: ; because It is a local minimum point, therefore and , This is the curvature of the radiation pattern at the null. Therefore, near the null, the shape of the radiation pattern is mainly determined by its second derivative. If It's very big. It only needs to be very small to make A rapid rise corresponds to a narrow zero-point dip. If Very small It needs to be very big to make A significant rise occurs, corresponding to a wide zero-depression. First, calculate... The first and second derivatives of are given by... and Then we have: ; Because at the zero point ,Right now ,so Its second derivative is: ; because ,therefore In order to explicitly obtain right The dependency relationship, for the first Interference plus noise covariance matrix Using the matrix inversion lemma to... Inverse equation: ; Will Substitute into In the middle, temporarily ignore The scalar normalization factor in the denominator is denoted as Then we have: ; To simplify the analysis, define .when Increase from 0 to hour, It is a monotonically increasing value from 0 to... If the variable is a variable, then use the variable. This can then be simplified to: ; Directly differentiating the above equation is quite complex, but it can be solved by analyzing its... We observe its changing trend by examining its extreme behavior over time, that is: ; It can be observed that the above equation converges to a fixed, non-zero constant. When In order to completely eliminate this infinitely large interference, the weight vector... It must be orthogonal to the steering vector of the disturbance. ,Right now At the same time, in order to satisfy The guide vector needs to be Projected onto In orthogonal subspaces, the projection operator is That is, the weight vector in the limiting case. To be proportional to .when When it is very small, the weight vector Approximately static At this point, the curvature is very small. With... The increase, Gradually approaching the weight vector It can be proven that curvature It is about It is a monotonically increasing function that starts from a small value and saturates towards a maximum value.

[0035] The method of this invention verifies the conclusion that increasing the interference power will increase the null depth and narrow the null width through simulation experiments. The simulation parameters are shown in Table 1. The interference-to-noise ratios of the three interference signals are 15dB, 20dB, 30dB, and 40dB.

[0036] Table 1 Simulation Parameter Settings

[0037] Figure 2 This is a schematic diagram of the simulation results, where, Figure 2 (a) is a schematic diagram of the simulation results when the interference signal has a noise ratio of 15dB. Figure 2 (b) is a schematic diagram of the simulation results when the interference signal interference-to-noise ratio is 20dB. Figure 2 (c) is a schematic diagram of the simulation results when the interference signal interference-to-noise ratio is 30dB. Figure 2 (d) is a schematic diagram of the simulation results when the interference signal interference-to-noise ratio is 40dB. From the simulation results, it can be seen that: as the interference power increases, the null depth formed by the adaptive beamformer becomes deeper, and for every increase in interference power... dB, null depth approximately increases dB, but the width of the zero trap will become narrower.

[0038] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0039] In one embodiment, an apparatus for analyzing the interference power effect on the performance of an adaptive beamformer is provided, comprising: The signal modeling module is used to construct a received signal vector containing the desired signal, interference signal and noise based on the pre-set array receiving scenario, calculate the covariance matrix of the received signal and separate the joint covariance matrix of interference and noise. The quantitative analysis module is used to determine beamforming weights based on the minimum variance distortionless response criterion and the joint covariance matrix of interference and noise. It uses matrix inversion and subspace decomposition methods to calculate the quantitative relationship between interference power and null depth based on the beamforming weights. The qualitative analysis module is used to calculate the curvature of the beam pattern at the null using the derivatives of the beamforming weights and the interference steering vector through Taylor expansion. Based on the correlation between curvature and null width, the qualitative relationship between interference power and null width is obtained. The results output module is used to output the analysis results of the interference power on the performance of the adaptive beamformer based on quantitative and qualitative relationships.

[0040] Specific limitations regarding the effect of interference power on the adaptive beamformer performance analysis device can be found in the above section on the limitations of the interference power effect on the adaptive beamformer performance analysis method, and will not be repeated here. Each module in the aforementioned interference power effect on the adaptive beamformer performance analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0041] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a method for analyzing the performance of an adaptive beamformer based on interference power. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0042] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0043] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0044] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0045] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0046] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0047] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing the effect of interference power on the performance of an adaptive beamformer, characterized in that, The method includes: Based on the pre-set array receiving scenario, a received signal vector containing the desired signal, interference signal and noise is constructed, the covariance matrix of the received signal is calculated, and the joint covariance matrix of interference and noise is obtained. Based on the minimum variance distortionless response criterion, the beamforming weights are determined using the joint covariance matrix of the interference and noise. Then, using matrix inversion and subspace decomposition methods, the quantitative relationship between interference power and null depth is calculated based on the beamforming weights. Using the derivatives of the beamforming weights and the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion. Based on the correlation between the curvature and the null width, a qualitative relationship between the interference power and the null width is obtained. Based on the quantitative and qualitative relationships described above, the analysis results of the impact of output interference power on the performance of the adaptive beamformer are presented.

2. The method for analyzing the effect of interference power on the performance of an adaptive beamformer according to claim 1, characterized in that, The array receiving scenario includes a uniform linear array scenario, wherein the uniform linear array contains N array elements, and the spacing between adjacent array elements is less than half the wavelength of the received signal, where N is a positive integer greater than 1.

3. The method for analyzing the performance of an adaptive beamformer based on interference power according to claim 1, characterized in that, The desired signal is a far-field narrowband signal, the interference signal is a K-channel far-field narrowband interference signal, and the noise is additive white Gaussian noise. The desired signal, the interference signal, and the noise are uncorrelated, where K is a positive integer.

4. The method for analyzing the performance of an adaptive beamformer based on interference power according to claim 1, characterized in that, The method of matrix inversion and subspace decomposition, which calculates the quantitative relationship between interference power and null depth based on the beamforming weights, includes: The joint covariance matrix of the interference and noise is decomposed into the sum of the residual covariance matrix and the target interference term; the residual covariance matrix contains other interference and noise components besides the interference to be analyzed, and the target interference term is the product of the power of the interference to be analyzed and the outer product of the corresponding steering vector. Based on the minimum variance distortionless response criterion and the sum of the residual covariance matrix and the target interference term, the beamforming weights are calculated using the matrix inversion lemma; the expression for the beamforming weights includes the power parameters of the interference to be analyzed. Substituting the beamforming weights into the beam gain formula for the interference direction, a functional relationship between beam gain and interference power to be analyzed is obtained. Based on the functional relationship, the attenuation trend of the gain in the interference direction as the interference power increases is determined, and a qualitative relationship between null depth and interference power is obtained. The interference subspace and noise subspace in the joint covariance matrix are separated by a subspace decomposition method. Based on the functional relationship and the qualitative relationship between the null depth and the interference power, the quantitative relationship between the null depth and the interference power is obtained.

5. The method for analyzing the performance of an adaptive beamformer based on interference power according to claim 4, characterized in that, Based on the aforementioned functional relationship, the attenuation trend of the interference direction gain as the interference power increases is determined, and the qualitative relationship between the null depth and the interference power is obtained, including: A magnitude analysis is performed on the functional relationship between the beam gain and the interference power to be analyzed to determine the growth order of the numerator and denominator terms in the functional relationship; wherein, the numerator term is a constant term that is independent of the interference power to be analyzed, and the denominator term is a term that grows quadratically with the increase of the interference power to be analyzed. Based on the difference in magnitude between the numerator and denominator, a qualitative relationship is obtained in which the depth of the null trap increases monotonically with the increase of the interference power to be analyzed.

6. The method for analyzing the effect of interference power on the performance of an adaptive beamformer according to claim 4, characterized in that, The interference subspace and noise subspace in the joint covariance matrix are separated using a subspace decomposition method. Based on the functional relationship and the qualitative relationship between the null depth and interference power, the quantitative relationship between the null depth and interference power is obtained, including: The joint covariance matrix of the interference and noise is decomposed into eigenvalues ​​and corresponding eigenvectors. Eigenvectors with eigenvalues ​​greater than a preset noise power threshold are classified as interference subspace. Determine the synchronous scaling relationship between the interference power to be analyzed and the corresponding feature value in the interference subspace, use the synchronous scaling relationship to analyze the scaling law of the interference directional gain with the interference power in the functional relationship, and use the scaling law to convert the interference directional gain into the interference power decibel value; Based on the linear correspondence between the change in interference power in dB and the change in null depth in dB, a quantitative relationship is obtained that when the interference power to be analyzed increases by n dB, the null depth increases by 2n dB.

7. The method for analyzing the performance of an adaptive beamformer based on interference power according to claim 1, characterized in that, Using the derivatives of the beamforming weights and the interference steering vector, the curvature of the beam pattern at the null is calculated through Taylor expansion. Based on the correlation between the curvature and the null width, the qualitative relationship between the interference power and the null width is obtained, including: Based on the array element distribution, the first derivative of the interference steering vector with respect to the angle of arrival is calculated, and the derivative reflects the rate of change of the steering vector with the angle offset; By performing a second-order Taylor expansion of the beam pattern at the angle of interference, the curvature coefficient expression related to the squared term of the angle offset in the expansion is obtained. Substituting the beamforming weights and the first derivatives into the curvature coefficient expression, the correlation between curvature and beamforming weights is obtained. Based on the correlation between curvature and beamforming weights, a qualitative relationship is obtained that the null width gradually narrows as the interference power increases.

8. A device for analyzing the performance of an adaptive beamformer based on interference power, characterized in that, The device includes: The signal modeling module is used to construct a received signal vector containing the desired signal, interference signal and noise based on the pre-set array receiving scenario, calculate the covariance matrix of the received signal and separate the joint covariance matrix of interference and noise. The quantitative analysis module is used to determine the beamforming weights based on the minimum variance distortionless response criterion using the joint covariance matrix of the interference and noise, and to calculate the quantitative relationship between the interference power and the null depth using matrix inversion and subspace decomposition methods based on the beamforming weights. The qualitative analysis module is used to calculate the curvature of the beam pattern at the null using the derivative of the beamforming weight and the interference steering vector through Taylor expansion, and to obtain the qualitative relationship between the interference power and the null width based on the correlation between the curvature and the null width. The results output module is used to output the analysis results of the interference power on the performance of the adaptive beamformer based on the quantitative and qualitative relationships.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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