A radar main lobe jamming suppression method based on direction constraint

Abstract

The application particularly relates to a radar main lobe interference suppression method based on direction constraint, which realizes suppression of interference and separation of target echo in a main lobe suppression type interference scene by embedding array manifold vectors into FastICA fixed point iteration and applying direction constraint to the separation vectors after each update so that the separation vectors are always focused on a target angle subspace.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and specifically to a radar main lobe interference suppression method based on directional constraints. Background Technology

[0002] In related technologies, the detection performance of radar systems is severely threatened when faced with interference. Mainloole suppression interference enters the receiver directly through the radar antenna's main lobe, making it difficult to distinguish the target signal from the interference signal in the spatial domain, thus affecting the radar's target detection performance.

[0003] In some technical solutions, blind source separation (BSS) technology has shown great potential in radar anti-jamming due to its ability to recover source signals from mixed received signals without prior information. Among these, the Fast Independent Component Analysis (FastICA) method employs a fixed-point iterative approach, enabling the separated output components to reach their maxima under non-Gaussian metrics such as kurtosis or negative entropy, offering advantages such as fast convergence speed and simple implementation. However, in strong interference scenarios, traditional FastICA methods may encounter problems such as unsatisfactory separation results or aliasing of the separated source signals.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a radar main lobe interference suppression method based on directional constraints and a computer program product, which can effectively overcome the defects existing in the prior art.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a radar main lobe interference suppression method based on directional constraints is provided, the method comprising: Configure the corresponding received data matrix for the received mixed signal; and perform centering and whitening processing on the received data matrix to obtain the corresponding whitening matrix; The target angle range is sampled at a preset step size to obtain a candidate direction set; and the candidate direction set is whitened based on the whitening matrix to determine the whitening array manifold vector. Configure the unit initial vector; using the whitened array manifold vector as the spatial direction constraint, project the unit initial vector onto the whitened array manifold vector of the candidate direction set to obtain the updated separation vector; Construct a separation matrix based on the updated separation vectors; and determine the source signal based on the separation matrix and the whitening vector.

[0008] In some exemplary embodiments, the received data matrix is ​​centered and whitened to obtain a corresponding whitening matrix, including: For the received data matrix Perform mean removal processing on each row to obtain the corresponding centralized data. ; Determine centralized data The corresponding covariance matrix And the covariance matrix Perform eigenvalue decomposition to determine the diagonal matrix. and eigenvector matrix ; Based on diagonal matrix and eigenvector matrix For centralized data Perform whitening processing and obtain the whitening matrix. .

[0009] In some exemplary embodiments, whitening is performed on the candidate direction set based on the whitening matrix to determine the whitening array manifold vector, including: The whitening matrix is ​​used to whiten the guiding vectors corresponding to each candidate direction, and the array manifold vectors corresponding to each candidate direction are obtained, including:

[0010] in, Represented as candidate directions The directional guide vector; It is represented as a whitening matrix.

[0011] In some exemplary embodiments, using the whitening array manifold vector as a spatial direction constraint, the unit initial vector is projected onto the whitening array manifold vector of the candidate direction set to obtain the updated separation vector, including: Configure the nonlinear function and its corresponding derivative; Based on the nonlinear function and its corresponding derivative, the current separation vector is updated by combining the whitening matrix and the unit initial vector, and the intermediate update vector is obtained. Project the intermediate update vector onto the whitening array manifold of the candidate direction set to determine the projection of the intermediate update vector onto each candidate direction; The target direction is determined based on the projection results, and an intermediate separation vector based on the direction constraints is also determined. The intermediate component vector and several converged component vectors are combined and orthogonalized and normalized to obtain an updated separation vector.

[0012] In some exemplary embodiments, the method further includes: When it is determined that the separation vector updated in two adjacent iterations is less than a preset threshold, the currently updated separation vector is configured with a converged separation vector; or When the separation vector of two adjacent iterations is determined to be greater than or equal to a preset threshold, the unit initial vector is updated, and the separation vector is iteratively updated based on the updated unit initial vector.

[0013] In some exemplary implementations, the current separation vector is updated based on a nonlinear function and its corresponding derivative, combined with a whitening matrix and a unit initial vector, to obtain an intermediate update vector, including:

[0014] in, Represented as the first A number of separate vectors; Represented as the whitened received data matrix; Represented as a nonlinear function; It represents the derivative of a nonlinear function.

[0015] In some exemplary embodiments, projecting the intermediate update vector onto the whitening array manifold of the candidate direction set to determine the projection of the intermediate update vector onto each candidate direction includes: Configure the projection matrix according to the whitening array manifold corresponding to the candidate direction, including:

[0016] in, Represented as the first Candidate directions The projection matrix; Represented as the first Candidate directions The whitening array manifold vector; Configure the projection of the intermediate update vector onto the candidate direction based on the projection matrix, including:

[0017] in, This is represented as an intermediate update vector.

[0018] In some exemplary implementations, configuring the unit initial vector includes: Configure the initial unit vector in a random manner; and configure the separation vector for the target number.

[0019] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-described method for suppressing radar main lobe interference based on directional constraints.

[0020] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for suppressing radar main lobe interference based on directional constraints.

[0021] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the above-described direction-constrained radar main lobe interference suppression method by executing the executable instructions.

[0022] The radar main lobe interference suppression method based on direction constraints provided in the embodiments of the present invention, after whitening processing, configures a unit initial vector and projects candidate directions using the whitened array manifold vector as a spatial direction constraint. Based on FastICA fixed-point iteration, a direction constraint mechanism is introduced, incorporating the array manifold prior into the update process of the separation vector, ensuring that the separation results satisfy statistical independence while maintaining spatial direction consistency. In strong main lobe suppression interference scenarios, compared with traditional methods, this method can effectively separate the target echo signal and suppress main lobe interference, effectively improving the radar's detection performance in strong main lobe interference environments.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0025] Figure 1 The diagram illustrates an exemplary embodiment of the present invention: a radar main lobe interference suppression method based on directional constraints. Figure 2 This schematic diagram illustrates a mixed signal received by a radar in an exemplary embodiment of the present invention; Figure 3This diagram illustrates a comparison between two signals obtained after separation under strong suppression interference using a conventional method in an exemplary embodiment of the present invention and the actual signal. Figure 4 This diagram illustrates the pulse compression and range-Doppler results of the first signal obtained after separation under strong suppression interference using a conventional method in an exemplary embodiment of the present invention. Figure 5 This diagram illustrates the pulse compression and range-Doppler results of the second signal obtained after separation under strong suppression interference using a conventional method in an exemplary embodiment of the present invention. Figure 6 This diagram illustrates a comparison between two signals obtained after separation under strong suppression interference, according to an exemplary embodiment of the present invention, and the actual signal. Figure 7 This schematic diagram illustrates the pulse compression and range-Doppler results of the first source signal obtained after separation under strong suppression interference using an exemplary embodiment of the present invention. Figure 8 This schematic diagram illustrates the pulse compression and range-Doppler results of the second source signal obtained after separation under strong suppression interference according to an exemplary embodiment of the present invention. Figure 9 The diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0027] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] In existing technologies, FastICA is based on the Independent Component Analysis (ICA) method that maximizes negative entropy. Its core is to iteratively solve for the projection direction that maximizes the non-Gaussianity metric. However, after whitening, it only aims to maximize the non-Gaussianity of the signal. The convergent solution may be an arbitrary separation vector. Although the vector is mathematically valid, it may not be aligned with the physical guide vector of the array, thus losing physical interpretability. At the same time, the separated signal will also produce aliasing.

[0029] To address the shortcomings and deficiencies of existing technologies, this example implementation provides a radar main lobe interference suppression method based on directional constraints, realizing a blind source separation method that embeds directional constraints into FastICA fixed-point iteration. (Reference) Figure 1 As shown, the method includes: Step S11: Configure the corresponding received data matrix for the received mixed signal; and configure the received data matrix... Perform centralization and whitening processes to obtain the corresponding whitening matrix; Step S12: Sample the target angle range according to a preset step size to obtain a candidate direction set; and perform whitening processing on the candidate direction set based on the whitening matrix to determine the whitening array manifold vector; Step S13: Configure the unit initial vector; using the whitening array manifold vector as the spatial direction constraint, project the unit initial vector onto the whitening array manifold vector of the candidate direction set to obtain the updated separation vector; Step S14: Construct a separation matrix based on the updated separation vectors; and determine the source signal based on the separation matrix and the whitening vector.

[0030] The method provided in this embodiment, for an array receiving signal model, originates from a certain direction. The response of the signal in the array space is determined by the array manifold vector. Description. If the target is located within a preset angular range, the corresponding separation vector should be consistent with, or at least as close as possible to, the orientation space spanned by the array manifold within that angular range. Therefore, this method introduces an orientation constraint mechanism based on FastICA fixed-point iteration, incorporating the array manifold prior into the update process of the separation vector, so that the separation results satisfy statistical independence while also taking into account spatial orientation consistency. This method is equivalent to optimizing the same type of contrast function over a smaller feasible region, thus significantly reducing the search space and improving the stability of the iteration.

[0031] The system and the steps of the method in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0032] In step S11, a corresponding received data matrix is ​​configured for the received mixed signal; and the received data matrix is ​​centered and whitened to obtain the corresponding whitening matrix.

[0033] For example, the received data matrix is ​​centered and whitened to obtain the corresponding whitening matrix, including: Step S21, for the received data matrix Perform mean removal processing on each row to obtain the corresponding centralized data. ; Step S22, determine centralized data The corresponding covariance matrix And the covariance matrix Perform eigenvalue decomposition to determine the diagonal matrix. and eigenvector matrix ; Step S23, based on the diagonal matrix and eigenvector matrix For centralized data Perform whitening processing and obtain the whitening matrix. .

[0034] Specifically, the above method can be applied to processing radar signals. It can be used by intelligent terminal devices connected to radar equipment or by the computing unit of the radar system itself to process the radar signals received in real time.

[0035] Specifically, for the currently received mixed signal, which may include mixed signal target echo, noise, and suppression interference, its data matrix can be defined as follows: ,in, M Number of receive channels T This represents the total number of sampling points. For the received data matrix... The first step is to perform mean removal to obtain centralized data. .

[0036] Then, the covariance matrix can be calculated using the centralized data, expressed by the formula:

[0037] The covariance matrix can be eigenvalued, and the formula is as follows:

[0038] in, The eigenvector matrix is ​​an orthogonal matrix. It is by eigenvalues A diagonal matrix formed.

[0039] Construct the whitening matrix using the following formulas:

[0040] Using whitening matrix for centered data Whitening processing is performed, and the whitened received data matrix is ​​obtained as the whitened data. The formula is expressed as follows:

[0041] in, This represents the received data matrix after whitening.

[0042] In step S12, the target angle range is sampled according to a preset step size to obtain a set of candidate directions; and the set of candidate directions is whitened based on the whitening matrix to determine the whitening array manifold vector.

[0043] Specifically, the range of angles of interest can be planned based on the operating range of the radar equipment. ; and increment the range of this angle by step. Perform equal-interval sampling to obtain the result from The set of candidate directions is represented as: .

[0044] The whitening matrix is ​​used to whiten the guide vectors of each candidate direction, resulting in a whitened array manifold vector, which is represented as:

[0045] in, Represented as candidate directions The directional guide vector; It is represented as a whitening matrix.

[0046] In step S13, a unit initial vector is configured; using the whitening array manifold vector as a spatial direction constraint, the unit initial vector is projected onto the whitening array manifold vector of the candidate direction set to obtain the updated separation vector.

[0047] For example, configuring a unit initial vector includes: configuring the unit initial vector in a random manner; and configuring a separation vector for the target number.

[0048] For example, using the whitening array manifold vector as a spatial direction constraint, the unit initial vector is projected onto the whitening array manifold vector of the candidate direction set to obtain the updated separation vector, including: Step S31: Configure the nonlinear function and its corresponding derivative; Step S32: Based on the nonlinear function and its corresponding derivative, the current separation vector is updated by combining the whitening matrix and the unit initial vector to obtain the intermediate update vector; Step S33: Project the intermediate update vector onto the whitening array manifold of the candidate direction set to determine the projection of the intermediate update vector on each candidate direction. Step S34: Determine the target direction based on the projection results, and determine the intermediate separation vector based on the direction constraints; Step S35: Combine the intermediate component vector and several converged component vectors, perform orthogonalization and normalization processing to obtain an updated separation vector.

[0049] For example, the method further includes: Step S36: When it is determined that the separation vector updated in two adjacent iterations is less than a preset threshold, the currently updated separation vector is configured with a converged separation vector; or Step S37: When it is determined that the separation vector of two adjacent iterations is greater than or equal to a preset threshold, the unit initial vector is updated, and the separation vector is iteratively updated based on the updated unit initial vector.

[0050] Specifically, we can first configure the nonlinear function and its derivative, expressed as:

[0051]

[0052] When performing single-component extraction, the number of separation vectors can be configured according to actual business needs, the specific objects detected by radar, and the application scenario, or in a random manner. The number of separation vectors can represent the number of source signals.

[0053] For the current need to solve the [i]th [e]th [i ... Separation vectors It can be that a unit initial vector is configured in a random manner. The current separation vector is updated using this initial unit vector. The formula is expressed as:

[0054] in, Represented as the first A number of separate vectors; Represented as a whitening matrix; Represented as a nonlinear function; It represents the derivative of a nonlinear function.

[0055] To introduce spatial orientation constraints, the intermediate update vector is... Project onto the whitening array manifold corresponding to the candidate direction set.

[0056] For the Candidate directions Its projection matrix is ​​expressed as:

[0057] in, .

[0058] Intermediate update vector The projection in this direction is represented as:

[0059] in, This is represented as an intermediate update vector.

[0060] Based on the projection calculation results, the projection norms of each candidate direction are compared, and the candidate direction that is closest to the separating vector is selected as the target candidate direction. The formula is expressed as:

[0061] Based on the selected candidate directions, the intermediate separation vector after applying directional constraints can be obtained, expressed as:

[0062] In the At that time, it is necessary to With the previously obtained The separated vectors are subjected to Gram-Schmidt orthogonalization and normalization, as expressed by the formula:

[0063]

[0064] Then, the result of the current calculation can be judged. If two adjacent iterations satisfy the preset rules, then the result is considered to be... The separating vectors have converged. The judgment rule can be expressed as:

[0065] in, Custom threshold.

[0066] Alternatively, if it is determined that two consecutive iterations do not meet the above threshold, the process can return to the above-mentioned randomly configured unit initial vector and restart the iteration process until the iteration termination condition is met.

[0067] In addition, for In this case, for the first separating vector, the result of its first intermediate separating vector calculation can be directly configured as the result of the corresponding separating vector calculation, and thus configured as the convergence result.

[0068] In step S14, a separation matrix is ​​constructed based on the updated separation vectors; and the source signal is determined based on the separation matrix and the whitening vector.

[0069] Specifically, after converging all N component vectors using the method described above, a separation matrix can be constructed, represented as: .

[0070] Then, the source signal can be determined using this separation matrix and whitening data, expressed by the formula:

[0071] The method provided by this invention, after generating an intermediate vector in each fixed-point iteration, uses the array manifold vector (i.e., the guiding vector of the desired or potential signal source) as a directional constraint projection and embeds it into the iterative optimization process of the FastICA method. Compared with the traditional FastICA method, this method can effectively separate the target echo signal and suppress main lobe interference, thus improving the radar's detection performance in environments with strong main lobe interference. (Reference) Figures 2-8 The signal separation embodiment shown in the invention demonstrates that the method of the present invention has excellent separation performance in single target and single interference scenarios. It can also be applied to multi-target and multi-interference source environments, greatly improving the radar's anti-interference capability.

[0072] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0073] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0074] Figure 9 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0075] It should be noted that, Figure 9 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0076] like Figure 9As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0077] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0078] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0079] Specifically, the aforementioned electronic devices can be airborne intelligent electronic devices, such as airborne video processing equipment.

[0080] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0081] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0082] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0083] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0084] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0085] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0086] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0087] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A radar main lobe interference suppression method based on directional constraints, characterized in that, The method includes: Configure the corresponding received data matrix for the received mixed signal; and perform centering and whitening processing on the received data matrix to obtain the corresponding whitening matrix; The target angle range is sampled at a preset step size to obtain a candidate direction set; and the candidate direction set is whitened based on the whitening matrix to determine the whitening array manifold vector. Configure the unit initial vector; using the whitened array manifold vector as the spatial direction constraint, project the unit initial vector onto the whitened array manifold vector of the candidate direction set to obtain the updated separation vector; Construct a separation matrix based on the updated separation vectors; and determine the source signal based on the separation matrix and the whitening vector.

2. The method according to claim 1, characterized in that, The received data matrix is ​​centered and whitened to obtain the corresponding whitening matrix, including: For the received data matrix Perform mean removal processing on each row to obtain the corresponding centralized data. ; Determine centralized data The corresponding covariance matrix And the covariance matrix Perform eigenvalue decomposition to determine the diagonal matrix. and eigenvector matrix ; Based on diagonal matrix and eigenvector matrix For centralized data Perform whitening processing and obtain the whitening matrix. .

3. The method according to claim 1, characterized in that, The candidate direction set is whitened based on the whitening matrix to determine the whitening array manifold vector, including: The whitening matrix is ​​used to whiten the guiding vectors corresponding to each candidate direction, and the array manifold vectors corresponding to each candidate direction are obtained, including: in, Represented as candidate directions The directional guide vector; It is represented as a whitening matrix.

4. The method according to claim 1, characterized in that, Using the whitening array manifold vector as a spatial directional constraint, the unit initial vector is projected onto the whitening array manifold vector of the candidate direction set to obtain the updated separation vector, including: Configure the nonlinear function and its corresponding derivative; Based on the nonlinear function and its corresponding derivative, the current separation vector is updated by combining the whitening matrix and the unit initial vector, and the intermediate update vector is obtained. Project the intermediate update vector onto the whitening array manifold of the candidate direction set to determine the projection of the intermediate update vector onto each candidate direction; The target direction is determined based on the projection results, and an intermediate separation vector based on the direction constraints is also determined. The intermediate component vector and several converged component vectors are combined and orthogonalized and normalized to obtain an updated separation vector.

5. The method according to claim 4, characterized in that, The method further includes: When it is determined that the separation vector updated in two adjacent iterations is less than a preset threshold, the currently updated separation vector is configured with a converged separation vector; or When the separation vector of two adjacent iterations is determined to be greater than or equal to a preset threshold, the unit initial vector is updated, and the separation vector is iteratively updated based on the updated unit initial vector.

6. The method according to claim 4, characterized in that, Based on the nonlinear function and its corresponding derivative, the current separation vector is updated using the whitening matrix and the unit initial vector, and intermediate update vectors are obtained, including: in, Represented as the first A number of separate vectors; Represented as the whitened received data matrix; Represented as a nonlinear function; It represents the derivative of a nonlinear function.

7. The method according to claim 1, characterized in that, Projecting the intermediate update vector onto the whitening array manifold of the candidate direction set determines the projection of the intermediate update vector onto each candidate direction, including: Configure the projection matrix according to the whitening array manifold corresponding to the candidate direction, including: in, Represented as the first Candidate directions The projection matrix; Represented as the first Candidate directions The whitening array manifold vector; Configure the projection of the intermediate update vector onto the candidate direction based on the projection matrix, including: in, This is represented as an intermediate update vector.

8. The method according to claim 1, characterized in that, Configure the unit initial vector, including: Configure the initial unit vector in a random manner; and configure the separation vector for the target number.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the radar main lobe interference suppression method based on directional constraints as described in any one of claims 1 to 8.

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