An Adaptive Beam Generation Method and System

Abstract

An adaptive beamforming method disclosed by the present invention mainly solves the problem of the output SINR decrease caused by the array manifold mismatch in adaptive beamforming. The implementation process is as follows: using a uniform linear array to collect training data; constructing a spatial blocking matrix by means of the prior angle information of the target; preprocessing the training data with the blocking matrix and calculating the interference covariance matrix based on the minimum power criterion; using matrix projection transformation to perform eigenvalue decomposition on the interference subspace matrix; and optimizing the beamforming weight vector by combining the idea of spatial response invariance. When there is a mismatch in the array manifold, the present invention can output the target without distortion on the premise of ensuring the anti-interference ability, and can be used to realize adaptive beamforming in the presence of the angle of arrival and array calibration errors.

Description

Technical Field

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

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

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

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

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

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

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

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

[0009] The object of the present invention is to provide an adaptive beam generation method and system, which can be used for adaptive beamforming in the presence of DOA and array calibration errors.

[0010] To solve the problem of the decrease in the output SINR caused by the steering vector mismatch in adaptive beamforming in the above problems, in a first aspect, the present invention provides an adaptive beam generation method, including:

[0011] Combining with a uniform linear array to collect sampling data at a set time, where the sampling data includes a target data and multiple interference data detected by a detection beam emitted by a radar echoer;

[0012] Combining with a blocking matrix to process the sampling data to obtain an interference covariance matrix;

[0013] Processing the interference covariance matrix to obtain a beam weight vector;

[0014] Modify the radar echo configuration parameters according to the beam weight vector, so that the radar echo generates a corrected adaptive beam.

[0015] Furthermore, the adaptive beam generation method further includes:

[0016] Generate the blocking matrix according to the radar search configuration parameters, where the radar search configuration parameters include the target prior angle parameter, the angle search interval parameter, and the number of angle searches.

[0017] Furthermore, the process of processing the interference covariance matrix to obtain a beam weight vector includes:

[0018] Process the interference covariance matrix to obtain an interference subspace matrix;

[0019] Obtain the beam weight vector according to the interference subspace matrix.

[0020] Furthermore, the process of combining a blocking matrix to process the sampled data to obtain an interference covariance matrix includes:

[0021] Combine a blocking matrix to process the sampled data to obtain processed sampled data;

[0022] Process the processed sampled data according to a set rule to obtain the interference covariance matrix.

[0023] Furthermore, the process of processing the interference covariance matrix to obtain an interference subspace matrix includes:

[0024] Perform eigenvalue decomposition on the interference covariance matrix to obtain a plurality of main eigenvectors;

[0025] Perform projection transformation on the plurality of main eigenvectors to obtain the interference subspace matrix.

[0026] Furthermore, the process of obtaining the beam weight vector according to the interference subspace matrix includes:

[0027] Obtain an equation for adaptive beam optimization according to the interference subspace matrix;

[0028] Solve the equation using the sampled covariance matrix inversion algorithm to obtain the beam weight vector.

[0029] Furthermore, the set rule is the minimum power criterion.

[0030] In a second aspect, the present invention provides an adaptive beam generation system, including:

[0031] Sampling module: collecting sampling data at a set time in combination with a uniform linear array, wherein the sampling data includes a target data and a plurality of interference data;

[0032] Interference covariance module: processing the sampled data in combination with a blocking matrix to obtain an interference covariance matrix;

[0033] Weight vector calculation module: processes the interference covariance matrix to obtain a beam weight vector;

[0034] Beam correction module: corrects the radar echo configuration parameters according to the beam weight vector to generate a corrected adaptive beam.

[0035] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any step of the adaptive beamforming method when executing the computer program.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the adaptive beamforming methods.

[0037] Advantages of the present invention

[0038] The present invention provides an adaptive beamforming method and system, which utilizes a target priori angle to construct an angle-widened blocking matrix, then uses the blocking matrix to eliminate the target component of training data, calculates and corrects the quasi-interference covariance matrix, and then utilizes matrix projection transformation to reconstruct the interference covariance matrix, and then optimizes the beamforming weight vector in combination with the spatial response invariance idea to achieve adaptive beamforming. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 is a schematic flow chart of an adaptive beamforming method in an embodiment of the present invention;

[0041] Figure 2 is a schematic diagram of array receiving data in an embodiment of the present invention;

[0042] Figure 3It is a relationship diagram between the beam pattern and the incoming wave direction under the incoming wave direction error in two comparison methods in the embodiment of the present invention;

[0043] Figure 4 It is a relationship diagram between the beam pattern and the incoming wave direction under the incoming wave direction error and array element position error in two comparison methods in the embodiment of the present invention;

[0044] Figure 5 It is a relationship diagram between the beam pattern and the incoming wave direction under the incoming wave direction and amplitude-phase error in two comparison methods in the embodiment of the present invention.

[0045] Figure 6 It is a schematic structural diagram of an electronic device in an embodiment of the present invention. Detailed implementation manners

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

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

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

[0049] Collect sampling data at a set moment in combination with a uniform linear array, where the sampling data includes a target data detected by a detection beam emitted by a radar echoer and a plurality of interference data;

[0050] Process the sampling data in combination with a blocking matrix to obtain an interference covariance matrix;

[0051] Process the interference covariance matrix to obtain a beam weight vector;

[0052] Modify the radar echoer configuration parameters according to the beam weight vector so that the radar echoer generates a corrected adaptive beam.

[0053] In some other implementation manners, the adaptive beam generation method further includes:

[0054] Generate the blocking matrix according to the radar search configuration parameters, where the radar search configuration parameters include target prior angle parameters, angle search interval parameters, and the number of angle searches.

[0055] In some other embodiments, the processing the interference covariance matrix to obtain a beam weight vector includes:

[0056] Process the interference covariance matrix to obtain an interference subspace matrix;

[0057] Obtain the beam weight vector according to the interference subspace matrix.

[0058] In some other embodiments, the combining a blocking matrix to process the sampled data to obtain an interference covariance matrix includes:

[0059] Combine a blocking matrix to process the sampled data to obtain processed sampled data;

[0060] Process the processed sampled data according to a set rule to obtain the interference covariance matrix.

[0061] In some other embodiments, the processing the interference covariance matrix to obtain an interference subspace matrix includes:

[0062] Perform eigenvalue decomposition on the interference covariance matrix to obtain a plurality of principal eigenvectors;

[0063] Perform projection transformation on the plurality of principal eigenvectors to obtain the interference subspace matrix.

[0064] In some other embodiments, the obtaining the beam weight vector according to the interference subspace matrix includes:

[0065] Obtain an equation for adaptive beam optimization according to the interference subspace matrix;

[0066] Solve the equation using the sampling covariance matrix inversion algorithm to obtain the beam weight vector.

[0067] In some other embodiments, the set rule is the minimum power criterion.

[0068] On the other hand, the present invention provides an adaptive beam generation system, including:

[0069] Sampling module: Combine a uniform linear array to collect sampled data at a set time, where the sampled data includes a target data and a plurality of interference data;

[0070] Interference covariance module: Combine a blocking matrix to process the sampled data to obtain an interference covariance matrix;

[0071] Weight vector calculation module: processes the interference covariance matrix to obtain a beam weight vector;

[0072] Beam correction module: corrects the radar echo configuration parameters according to the beam weight vector to generate a corrected adaptive beam.

[0073] Adaptive beamforming technology focuses on the undistorted output of the target while ensuring sufficient suppression of interference, which requires accurate reconstruction of the interference covariance matrix and constraint of the target steering vector. See Figure 1 , the adaptive beam generation method includes the following steps:

[0074] Step (1) Collect training data: The training data collected by the uniform linear array at time t is X(t). The uniform linear array consists of M array elements arranged at half-wavelength intervals. The training data contains the echo information of one target and J interferences;

[0075] Step (2) Construct a blocking matrix: Use the prior angle information of the target Angle search interval Δ and the number of angle searches l to construct 2l + 1 target blocking matrices B i , i = -l, -l + 1, …, l;

[0076] Step (3) Calculate the interference covariance matrix: With the help of the blocking matrix B i , i = -l, -l + 1, …, l processes the training data to obtain the data after target removal, and selects the data after target removal based on the minimum power criterion to calculate the interference covariance matrix

[0077] Step (4) Decompose the interference subspace matrix: Perform eigenvalue decomposition on the interference covariance matrix to obtain the first J main eigenvectors, and perform projection transformation on these J main eigenvectors to estimate the interference subspace matrix

[0078] Step (5) Optimize the beamforming weight vector: Establish a flat-top main beam optimization equation in combination with the idea of spatial response invariance, and solve the equation to estimate the beamforming weight vector.

[0079] In a specific implementation manner, in step (3), the use of the blocking matrix B i , i = -l, -l + 1, …, l processes the training data to obtain the data after target removal, and selects the data after target removal based on the minimum power criterion to calculate the interference covariance matrix Specifically, it includes the following steps:

[0080] 3.1(a) With the help of the blocking matrix B i , i = -l, -l + 1, …, l processes the training data to obtain the data after target removal:

[0081] X i (t)=B i X(t), i = -l, -l + 1, …, l

[0082] 3.1(b) Select the data after target removal based on the minimum power criterion and calculate the interference covariance matrix

[0083] K is the number of snapshots, denotes the matrix corresponding to the minimum norm.

[0084] The interference covariance matrix described in step (4) Perform eigenvalue decomposition to obtain the first J principal eigenvectors, and perform projection transformation on these J principal eigenvectors to estimate the interference subspace matrix Specifically, it includes the following steps:

[0085] 4.1(a) Perform eigenvalue decomposition on the interference covariance matrix :

[0086]

[0087] η i , i = 1, 2, …, M represents the quasi-interference covariance matrix The eigenvalues of, which are arranged in descending order as η1 > η2 > … > η M-2 > … > η M , p i , i = 1, 2, …, M represents the corresponding eigenvectors;

[0088] 4.1(b) Perform projection transformation on these J principal eigenvectors to estimate the interference subspace matrix

[0089]

[0090] represents the blocking matrix corresponding to the data after target removal selected based on the minimum power criterion.

[0091] The steps of establishing the flat-top main beam optimization equation by combining the spatial response invariance idea in step (5), solving the equation to estimate the beamforming weight vector, specifically include the following steps:

[0092] 5.1(a) Establish the flat-top main beam optimization equation by combining the spatial response invariance idea:

[0093]

[0094]

[0095]

[0096] represents the spatial response invariant objective function represents the weight vector calculated by using the sampling covariance inversion method

[0097] 5.1(b) Solve the equation to estimate the beamforming weight vector

[0098]

[0099] β = w H w represents the normalization factor

[0100] The present invention will be further described below in conjunction with specific embodiments

[0101] Embodiment 1

[0102] Step 1: Refer to Figure 2 , and collect the training data X(t) at time t by using a uniform linear array composed of M array elements arranged at half-wavelength intervals

[0103] Step 2: Construct a blocking matrix, which is specifically implemented by using the target prior angle information angle search interval Δ and the number of angle searches l to construct 2l + 1 target blocking matrices B i , i = -l, -l + 1, …, l

[0104]

[0105] is the spatial steering vector of the incoming wave angle , represents the power adjustment factor, trace(·) represents matrix trace represents the sampling covariance matrix calculated by K snapshots, (·) H represents conjugate transpose, (·) T represents conjugate

[0106] Step 3: Calculate the interference covariance matrix, and the specific implementation is as follows

[0107] (3a) With the help of the blocking matrix B i , i = -l, -l + 1, …, l process the training data to obtain the data after target removal

[0108] X i (t) = B i X(t), i = -l, -l + 1, …, l, (2)

[0109] (3b) Calculate the interference covariance matrix based on the minimum power criterion for the data after selecting the target to be removed

[0110] K is the number of snapshots, represents the matrix corresponding to the minimum norm.

[0111] Step 4: Decompose the interference subspace matrix, and the specific implementation is as follows:

[0112] For the interference covariance matrix perform eigenvalue decomposition to obtain the first J main eigenvectors, and perform projection transformation on these J main eigenvectors to estimate the interference subspace matrix Specifically, it includes the following steps:

[0113] (4a) Perform eigenvalue decomposition on the interference covariance matrix :

[0114]

[0115] η i , i = 1, 2,..., M represents the quasi-interference covariance matrix of the eigenvalues, which are arranged in descending order as η1 > η2 >... > η M-2 >... > η M , p i , i = 1, 2,..., M represents the corresponding eigenvectors;

[0116] (4b) Perform projection transformation on these J main eigenvectors to estimate the interference subspace matrix

[0117]

[0118] represents the blocking matrix corresponding to the data after selecting the target to be removed based on the minimum power criterion.

[0119] Step 5: Optimize the beamforming weight vector, and the specific implementation is as follows:

[0120] (5a) Establish a flat-top main beam optimization equation by combining the idea of spatial response invariance:

[0121]

[0122] represents the spatial response invariance objective function, represents the weight vector calculated by using the sampling covariance inversion method;

[0123] (5b) Solve the equation to estimate the beamforming weight vector:

[0124]

[0125] β = w H w represents the normalization factor.

[0126] Example 2:

[0127] Experimental conditions: Uniform linear array (number of array elements M = 15), element spacing 0.5λ (λ = 0.05), number of signal sources 3, including one target with a true angle of 10° (prior angle 8°), two interferences with true angles of -25° and 40° (prior angles -23° and 38°), and interference-to-noise ratios of 30 dB for both; 200 Monte Carlo simulations are performed for each trial;

[0128] Simulation parameters: The angle search interval Δ and the number of angle searches l of the present invention are set to 0.5° and 10 respectively. The existing technologies for comparison are set as follows: Capon beamforming algorithm; Linearly Constrained Minimum Variance (LCMV) method, with the steering vector constraint angle being

[0129] Experimental results, when there is only an error in the incident direction of the target, the incident direction is incremented from -90° to 90°, the SNR is fixed at 20 dB, the number of snapshots is fixed at 50 times, and the relationship between the beam pattern and the incident direction is as Figure 3 shown;

[0130] Observation Figure 3 It is found that when there is only an error in the incident direction of the target, the present invention can form a flat top main lobe in the target area, and at the same time can form deep nulls for the interference. Although the Capon algorithm can form deep nulls, the main lobe cannot correctly point to the target, and the sidelobe level of the LCMV algorithm is very high, and the null level is also slightly elevated.

[0131] Example 3:

[0132] Experimental conditions and simulation parameters are the same as in Example 2, and at the same time, the experimental conditions also include element position errors that follow a uniform distribution of (-0.05λ, 0.05λ);

[0133] Experimental results, when there are both an error in the incident direction of the target and element position errors, the incident direction is incremented from -90° to 90°, the SNR is fixed at 20 dB, the number of snapshots is fixed at 50 times, and the relationship between the beam pattern and the incident direction is as Figure 4 shown;

[0134] Observation Figure 4It is found that when there are both target incoming wave direction errors and array element position errors, the present invention can form a flat-top main lobe in the target area and can form deep nulls for interference. Although the Capon algorithm can form deep nulls, the main lobe cannot correctly point to the target. The sidelobe level of the LCMV algorithm is very high, and the null level is also slightly elevated.

[0135] Embodiment 4:

[0136] The experimental conditions and simulation parameters are the same as those in Embodiment 2. At the same time, the experimental conditions also include amplitude-phase errors. The amplitude error follows a uniform distribution of (-5dB, 5dB), and the phase error follows a uniform distribution of (-5°, 5°);

[0137] The experimental results show that when there are both target incoming wave direction errors and amplitude-phase errors, the incoming wave direction is incremented from -90° to 90°, the SNR is fixed at 20dB, and the number of snapshots is fixed at 50 times. The relationship between the beam direction pattern and the incoming wave direction is as Figure 5 shown.

[0138] Observation Figure 5 It is found that when there are both target incoming wave direction errors and amplitude-phase errors, the present invention can form a flat-top main lobe in the target area and can form deep nulls for interference. Although the Capon algorithm can form deep nulls, the main lobe cannot correctly point to the target. The sidelobe level of the LCMV algorithm is very high, and the null level is also slightly elevated.

[0139] As can be seen from the above description, an adaptive generation method disclosed by the present invention mainly solves the problem of the decrease in output SINR caused by array manifold mismatch in adaptive beamforming. The implementation process is as follows: using a uniform linear array to collect training data; constructing a spatial blocking matrix with the help of target prior angle information; preprocessing the training data with the blocking matrix and calculating the interference covariance matrix based on the minimum power criterion; using matrix projection transformation to decompose the interference subspace matrix by eigenvalue decomposition; and optimizing the beamforming weight vector by combining the idea of spatial response invariance. When the array manifold is mismatched, the present invention can output the target without distortion while ensuring the anti-interference ability, and can be used to realize adaptive beamforming in the presence of angle of arrival and array calibration errors.

[0140] From the hardware level, in order to solve the problems that when there are constraint deviations in the target steering vector, in the case of low signal-to-noise ratio (SNR), problems such as main lobe deviation may occur, reducing the output SINR; in the case of high SNR, if the received data contains the target, there may even be a signal 'auto-cancellation' phenomenon, resulting in a sharp deterioration of the output SINR, this application provides an embodiment of an electronic device for implementing all or part of the content in the above generation method. The electronic device specifically includes the following content:

[0141] Figure 6 Schematic block diagram of the system composition of the electronic device 9600 according to an embodiment of the present application. As Figure 6 shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It should be noted that this Figure 6 is exemplary; other types of structures may also be used to supplement or replace this structure to achieve telecommunication functions or other functions.

[0142] In one embodiment, adaptive beam generation may be integrated into the central processing unit. Among them, the central processing unit may be configured to perform the following controls:

[0143] Combine a uniform linear array to collect sampling data at a set moment, where the sampling data includes a target data and multiple interference data detected by a detection beam emitted by a radar echoer;

[0144] Combine a blocking matrix to process the sampling data to obtain an interference covariance matrix;

[0145] Process the interference covariance matrix to obtain a beam weight vector;

[0146] Modify the radar echoer configuration parameters according to the beam weight vector so that the radar echoer generates a corrected adaptive beam.

[0147] In another embodiment, the generating device of the adaptive beam generation system may be separately configured from the central processing unit 9100. For example, the adaptive beam generation system may be configured as a chip connected to the central processing unit 9100 to perform the adaptive beam generation function under the control of the central processing unit.

[0148] As Figure 6 shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily have to include all the components shown in Figure 6 ; in addition, the electronic device 9600 may further include components not shown in Figure 6 , and reference may be made to the prior art.

[0149] As Figure 6 shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processing unit 9100 receives inputs and controls the operations of the various components of the electronic device 9600.

[0150] Among them, the memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. The above-mentioned information related to failures can be stored, and in addition, a program for executing relevant information can also be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to achieve information storage or processing, etc.

[0151] The input unit 9120 provides an input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display can be, for example, an LCD display, but is not limited thereto.

[0152] The memory 9140 can be a solid-state memory. For example, a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that stores information even when power is off, can be selectively erased and has more data. An example of this memory is sometimes referred to as an EPROM, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage unit 9142, which is used to store application programs and function programs or the processes for operating the electronic device 9600 through the central processing unit 9100.

[0153] The memory 9140 can also include a data storage unit 9143, which is used to store data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 can include various drivers of the electronic device for communication functions and / or for executing other functions of the electronic device (such as a messaging application, an address book application, etc.).

[0154] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide an input signal and receive an output signal, which can be the same as the case of a conventional mobile communication terminal.

[0155] Based on different communication technologies, in the same electronic device, multiple communication modules 9110 may be provided, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby implementing normal telecommunication functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers, etc. Additionally, the audio processor 9130 is also coupled to a central processor 9100, enabling recording on the device through the microphone 9132 and playing back the sounds stored on the device through the speaker 9131.

[0156] Embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the adaptive beam generation method in the above embodiments. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements all the steps of the adaptive generation method with the execution subject being a server or a client in the above embodiments. For example, when the processor executes the computer program, the following steps are implemented:

[0157] Collect sampling data at a set moment in combination with a uniform linear array, where the sampling data includes a target data and multiple interference data detected by a detection beam emitted by a radar echo device;

[0158] Process the sampling data in combination with a blocking matrix to obtain an interference covariance matrix;

[0159] Process the interference covariance matrix to obtain a beam weight vector;

[0160] Modify the radar echo device configuration parameters according to the beam weight vector so that the radar echo device generates a corrected adaptive beam.

[0161] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0162] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0163] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0165] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An adaptive beam generation method, characterized in that, Comprising: Collecting sampling data at a set moment by combining a uniform linear array arranged with M half-wavelengths, where the sampling data includes a target data and multiple interference data detected by a detection beam emitted by a radar echo device, and where M = 15; Generating a blocking matrix according to radar search configuration parameters, where the radar search configuration parameters include a target prior angle parameter, an angle search interval parameter, and a number of angle searches; Processing the sampling data in combination with a blocking matrix to obtain an interference covariance matrix; Processing the interference covariance matrix to obtain a beam weight vector; The processing the interference covariance matrix to obtain a beam weight vector includes Performing eigenvalue decomposition on the interference covariance matrix to obtain multiple principal eigenvectors; Performing projection transformation on the multiple principal eigenvectors to obtain an interference subspace matrix; Establishing a flat-top main beam optimization equation in combination with the idea of spatial response invariance, and obtaining the beam weight vector based on the flat-top main beam optimization equation; Correcting the radar echo device configuration parameters according to the beam weight vector so that the radar echo device generates a corrected adaptive beam.

2. The adaptive beam generation method according to claim 1, wherein The processing the sampling data in combination with a blocking matrix to obtain an interference covariance matrix includes: Processing the sampling data in combination with a blocking matrix to obtain processed sampling data; Processing the processed sampling data according to a set rule to obtain the interference covariance matrix.

3. The adaptive beam generation method according to claim 1, wherein The obtaining the beam weight vector according to the interference subspace matrix includes: Obtaining an equation for adaptive beam optimization according to the interference subspace matrix; Solving the equation using a sampling covariance matrix inversion algorithm to obtain the beam weight vector.

4. The adaptive beam generation method according to claim 2, wherein The set rule is the minimum power criterion.

5. An adaptive beam generation system, characterized in that, Comprising: Sampling module: Collecting sampling data at a set moment by combining a uniform linear array, where the sampling data includes a target data and multiple interference data, and where M = 15; Interference covariance module: Generating a blocking matrix according to radar search configuration parameters, where the radar search configuration parameters include a target prior angle parameter, an angle search interval parameter, and a number of angle searches, and processing the sampling data in combination with a blocking matrix to obtain an interference covariance matrix; Weight vector calculation module: Processing the interference covariance matrix to obtain a beam weight vector, including performing eigenvalue decomposition on the interference covariance matrix to obtain multiple principal eigenvectors, performing projection transformation on the multiple principal eigenvectors to obtain an interference subspace matrix, establishing a flat-top main beam optimization equation in combination with the idea of spatial response invariance, and obtaining the beam weight vector based on the flat-top main beam optimization equation; Beam correction module: Correcting the radar echo device configuration parameters according to the beam weight vector to generate a corrected adaptive beam.

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

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

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