Simulation method and system for open-architecture anti-jamming beamforming and target tracking

Abstract

The application discloses an anti-interference beam forming and target tracking joint simulation method and system based on an open architecture, relates to the field of audio signal processing, and divides the system into an array element data generation component, a beam forming component, a display and control component and a target tracking component through a modular joint simulation architecture, and defines a standardized interface to realize flexible interaction and dynamic cooperation among the modules. In the simulation process, an operator can seamlessly switch between a conventional beam forming algorithm and an anti-interference beam forming algorithm according to needs through a man-machine interface, and algorithm in-situ replacement is completed without interrupting the simulation. The anti-interference beam forming adopts a spatial spectrum sampling and interference noise covariance matrix reconstruction technology, and realizes stable target tracking in combination with Kalman filtering. The application effectively improves the robustness and feature distinguishability of target tracking under an interference background, supports flexible switching of multi-target display and single-target anti-interference, and significantly improves system-level simulation efficiency.

Description

Technical Field

[0001] This invention relates to the field of audio signal processing technology, specifically to a co-simulation method and system for anti-interference beamforming and target tracking based on an open architecture. Background Technology

[0002] In audio signal processing, conventional beamforming structures are simple and computationally efficient, but their ability to suppress strong interference from undesired directions is limited. When the interference energy is strong, the signal-to-interference-plus-noise ratio (SINR) of conventional beamforming output drops significantly, leading to performance degradation in subsequent target detection and tracking, and even problems such as target loss or a surge in false alarms. To improve anti-interference capabilities, methods such as Minimum Variance Distortionless Response (MVDR) and its improved forms are commonly used to create deep nulls in the direction of interference to suppress interference energy. These algorithms typically assume that the direction of the desired signal is known and fixed. However, in real dynamic environments, due to factors such as errors in estimating the direction of arrival of the desired signal and changes in array element positions, traditional MVDR algorithms often incorrectly identify the desired signal as interference, thereby reducing the output SINR of beamforming and degrading system performance. One solution involves reconstructing the interference plus noise covariance matrix to exclude the desired signal energy. When calculating the interference plus noise covariance matrix, the signals in the desired signal direction and its vicinity can be set to zero, completely avoiding the MVDR beamformer's erroneous suppression of the desired signal. However, this method can only achieve a minimum variance distortion-free response for a single target, making it difficult to balance anti-interference capability with multi-target (including interference) display capability. Furthermore, in existing system-level simulations or engineering systems, beamforming algorithms often run statically at the front end and are relatively bound to the overall system, lacking a dynamic collaborative mechanism with backend target tracking and corresponding human-computer interaction, thus hindering efficient system-level simulation. Summary of the Invention

[0003] This invention provides a joint simulation method for anti-interference beamforming and target tracking based on an open architecture, which solves the technical problem that the interference plus noise covariance reconstruction beamforming method is difficult to perform efficiently for underwater acoustic system simulation.

[0004] In a first aspect, the present invention provides a joint simulation method for anti-jamming beamforming and target tracking based on a modular open architecture, comprising: Construct a modular co-simulation architecture: Decompose the beamforming and target tracking co-simulation system into four functionally independent core modules: array data generation component, beamforming component, display and control component, and target tracking component; Standardized data and control interfaces are defined between modules, including: a data interface for transmitting 3D sound field data packets from the array data generation component to the beamforming component; a data interface for transmitting beam pattern data from the beamforming component to the display and control component and the target tracking component; a data interface for transmitting the desired signal wave angle estimate from the target tracking component to the beamforming component; a control interface for transmitting tracking commands and the initial azimuth of the target from the display and control component to the target tracking component; and a data interface for transmitting target trajectory information from the target tracking component to the display and control component. Perform a conventional beamforming simulation: The array element-level acoustic signal data is generated by the array element data generation component, the beamforming component generates beam domain data using a conventional beamforming algorithm, the display and control component displays the time-azimuth history map in real time, and the operator selects a potential target and triggers the target tracking component to perform target tracking. Seamless switching to anti-interference beamforming simulation: When the operator triggers the switching of the anti-interference beamforming algorithm through the display and control components, the current data batch number is recorded. After the beamforming component completes the processing of the current batch of data, it replaces the algorithm instance from the conventional beamforming algorithm to the anti-interference beamforming algorithm in place. The anti-interference beamforming algorithm is used for processing from the next batch of data. Other modules do not run interrupted during the switching process. Perform closed-loop simulation of anti-jamming beamforming and target tracking: The anti-jamming beamforming algorithm reconstructs the interference plus noise covariance matrix based on the expected signal arrival angle estimate fed back by the target tracking component, solves the MVDR array weighting vector and performs beamforming. The target tracking component updates the target state estimate based on the beammap data and feeds back the updated expected signal arrival angle estimate to the beamforming component to form a closed loop.

[0005] In some instances, standardized interfaces between modules adopt a data-driven model, where internal states are not shared between modules, and the interface data format uses a platform-independent, universal data description format.

[0006] In some instances, the algorithm instance is replaced in situ from a conventional beamforming algorithm with an anti-jamming beamforming algorithm, and the anti-jamming beamforming algorithm is used for processing from the next batch of data onwards. During the switching process, other modules do not run interrupted, including: The beamforming component internally uses a strategy pattern to manage multiple beamforming algorithm instances, while exposing a unified algorithm call interface to the outside world; When an algorithm switching instruction is received, the beamforming component switches the internally held algorithm instance pointer after the current data batch is processed, and calls the processing function of the new algorithm instance from the next batch of data. During the switching process, the input data buffer, output data queue, and interface connections with other modules of the beamforming component remain unchanged.

[0007] In some instances, the anti-jamming beamforming algorithm reconstructs the interference plus noise covariance matrix based on the expected signal angle of arrival estimate fed back by the target tracking component, solves the MVDR array weighting vector, and performs beamforming. The target tracking component updates the target state estimate based on the beammap data and feeds back the updated expected signal angle of arrival estimate to the beamforming component, forming a closed loop, including: Constructing the MZ sharpening matrix For the sampling covariance matrix Spatial spectral fuzzification is performed to obtain ; Based on the ambiguity of the direction of arrival of the desired signal supplement Constructing the sampling matrix ,right Spatial spectrum sampling was performed to obtain ; The sampling results are blurred again using the MZ sharpening matrix to obtain the reconstructed interference plus noise covariance matrix. ; Based on the reconstructed interference plus noise covariance matrix and desired signal steering vector Solve for the array weighting vector: ; The beam domain data is output by weighting and summing the array weighted vectors based on the array weighted vectors.

[0008] In some instances, the MZ sharpening matrix is ​​defined as: ,in, For the number of array elements, The fuzzy range parameter has a value of [value]. .

[0009] In some instances, the desired signal direction of arrival is ambiguous. The operator initially selects the direction. and preset fuzzy range Determined, that is ,in, The range of values ​​is to .

[0010] In some instances, the target tracking component employs a Kalman filter algorithm to achieve multi-target state estimation and outputs target trajectory parameters, including estimated values ​​for position, velocity, heading, and azimuth.

[0011] In some instances, the method further includes: the operator triggers the beamforming algorithm at any time via the display and control components to switch from anti-interference mode back to normal mode, achieving seamless bidirectional switching between the two modes, with the simulation proceeding uninterrupted throughout the entire process.

[0012] Secondly, the present invention provides a joint simulation system for anti-jamming beamforming and target tracking based on a modular open architecture, comprising: The array data generation component is used to generate array element-level acoustic signal data that conforms to physical characteristics based on preset array parameters, environmental parameters and target parameters, and output three-dimensional sound field data packets in batches. The beamforming component, connected to the array data generation component, is used to receive three-dimensional sound field data packets, perform beamforming processing according to the currently active beamforming algorithm, and output beammap data. The beamforming component internally uses a strategy mode to manage at least two beamforming algorithm instances, including conventional beamforming algorithms and anti-interference beamforming algorithms, and supports dynamic switching at runtime. The display and control components are connected to the beamforming components and the target tracking components, respectively. They are used to display the beam pattern and target trajectory in real time, and provide a human-machine interface for operators to select targets, enable or disable tracking, and trigger beamforming algorithm switching. The target tracking component is connected to the beamforming component and the display and control component respectively. It is used to perform target state estimation based on beam pattern data and tracking commands, combined with the target motion model, output target trajectory parameters, and feed back the estimated value of the expected signal arrival angle to the beamforming component. The components communicate with each other through standardized data and control interfaces, enabling the beamforming component to perform in-situ algorithm replacement during simulation without interrupting the operation of other components.

[0013] In some instances, when the beamforming component receives an algorithm switching instruction, it switches the internally held algorithm instance pointer after the current data batch is processed, and calls the processing function of the new algorithm instance from the next batch of data. During the switching process, the interface connection with other components remains unchanged.

[0014] In some instances, the anti-jamming beamforming algorithm is an interference-plus-noise covariance reconstruction MVDR beamforming algorithm based on spatial spectrum sampling, including: The MZ sharpening matrix construction module is used to perform spatial spectral blurring on the sampling covariance matrix. The spatial spectrum sampling module is used to construct a sampling matrix based on the complement of the ambiguous interval of the direction of arrival of the desired signal and to perform spatial spectrum sampling. The MVDR weighting module is used to solve for the array weighting vector based on the reconstructed interference plus noise covariance matrix; The beamforming execution module is used to output beam domain data based on the array weighted vector.

[0015] In some instances, the standardized data interface includes: The first data interface for the array data generation component to transmit three-dimensional sound field data packets to the beamforming component; A second data interface for the beamforming component to transmit beam pattern data to the display and control component; The third data interface for the beamforming component to transmit beam pattern data to the target tracking component; The target tracking component transmits the desired signal to the beamforming component via a fourth data interface that provides the estimated wave angle. The display and control component transmits tracking commands and the initial orientation of the target to the target tracking component via a control interface. The fifth data interface through which the target tracking component transmits target trajectory information to the display and control component.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1) Improve tracking robustness: Operator-guided anti-interference beamforming effectively suppresses interference energy, avoids the "signal cancellation" problem caused by leakage of desired signals in traditional adaptive methods, and ensures continuous and stable tracking of the target.

[0017] 2) Enhanced feature recognizability: The signal-to-interference-plus-noise ratio of the output signal is significantly improved, thereby enhancing the recognizability of target features.

[0018] 3) The proposed anti-interference beamforming component can replace the conventional beamforming algorithm in situ, enabling seamless switching of joint simulations without reconstructing the overall process. It can be activated only as needed to improve the anti-interference capability in specific scenarios, significantly improving simulation efficiency. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a system architecture diagram provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the co-simulation method provided in the embodiments of the present invention; Figure 3 This is a diagram of the seamless switching human-computer interaction interface of the beamforming algorithm provided in this embodiment of the invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the following steps and operations can also be implemented in hardware.

[0023] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. Different components, modules, engines, and services described herein can be considered as implementations on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0024] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0025] In this embodiment of the invention, a co-simulation method for anti-jamming beamforming and target tracking based on an open architecture is provided, which mainly includes the following parts: 1. Construct a modular co-simulation architecture Functional partitioning and module decomposition were performed, breaking down the beamforming and target tracking joint simulation into four functionally independent core modules, such as... Figure 1 As shown: 1) Array data generation component: By pre-setting underwater acoustic array parameters (including number of array elements, array shape, etc.), environmental parameters (seawater sound velocity profile, sea depth, seabed topography, seabed type, etc.), and target parameters (line spectrum, etc.), it generates array element-level acoustic signal data that conforms to physical characteristics and outputs a three-dimensional sound field data package containing time series and spatial coordinates. 2) Beamforming component: After receiving array element data packets, it uses a time delay-superposition algorithm for signal processing and outputs beam pattern data containing beam directivity, signal-to-noise ratio and spatial resolution; 3) Display and control components: The beam pattern and corresponding target trajectory are displayed in real time through a graphical interface; at the same time, human-computer interaction functions are provided, enabling operators to select targets and enable tracking, or delete selected targets and disable tracking; 4) Target tracking component: Based on beammap data, control commands from the real-time and control components, and combined with the target motion model, multi-target state estimation is achieved through the Kalman filter algorithm, and the target trajectory parameters (position, velocity, heading) are output. Standardize interface design and define control and data interfaces between modules: 1) Design a data interface to provide parameter input to the array element domain data, for preset array parameters and environmental parameters; 2) Design a data interface for the array element domain data generation component to send three-dimensional sound field data packets to the beamforming component in batches, for transmitting array element domain data for use in beamforming and subsequent processes; 3) Design a data interface for the beamforming component to send beam pattern data to the display and control component, so that the display and control component can display the time-azimuth history diagram; 4) Design a data interface for the beamforming component to send beam pattern data to the target tracking component for target tracking; 5) Design a data interface for the target tracking component to send the desired signal to the beamforming component at the desired wave angle, so that the beamforming component is compatible with MVDR-type beamforming algorithms; 6) Design an interface for the display and control components to send tracking commands and related data to the target tracking components, which is used to trigger the activation / deactivation of target tracking; 7) Design a data interface for the target tracking component to send target trajectory information to the display and control component, so that the display and control component can display the target trajectory; 2. Set simulation parameters and perform a conventional beamforming simulation. After completing the modular co-simulation architecture, run a conventional beamforming simulation in the following manner: 1) Inject preset parameters into the array element domain data generation component to simulate the characteristics of array elements, environment, and target; 2) The beamforming component receives array element domain data, executes a conventional beamforming algorithm (CBF), and generates beam domain data; 3) The display and control components receive beammap data and refresh the display of the time-azimuth history graph in real time; 4) The operator determines the direction of the desired signal based on the information from the human-machine interface, selects potential targets, and initiates target tracking; 5) The target tracking component performs target tracking and displays the trajectory of the potential target selected by the user on the display and control component; 3. Seamlessly switch to anti-interference beamforming simulation according to operator needs. Under interference, effective target tracking based on the operator-selected desired signal direction is impossible. The operator activates the human-machine interface to trigger the anti-jamming beamforming algorithm. Simultaneously, the system records the direction of the potential target selected by the operator and transmits this direction to the anti-jamming beamforming component via the target tracking component. Since both the anti-jamming and conventional beamforming algorithms are modular beamforming components with predefined standardized data interfaces compatible with both algorithms, from the moment the anti-jamming beamforming algorithm is triggered, the simulation system replaces the conventional beamforming algorithm module with the anti-jamming beamforming algorithm module in situ, without interruption of other modules, interfaces, or real-time data. The element domain data generation component sends element domain data in batches. After the in-situ component replacement is completed, the next batch of element domain data will begin executing the anti-jamming beamforming algorithm based on spatial spectrum sampling, as follows: 1) Reconstruct the interference plus noise covariance matrix When performing spatial spectrum sampling, due to the limited number of array elements and the insufficient density and large spacing of spatial spectrum observation points, the power spectrum of interference is blurred before sampling to expand the spectral peak range and enhance robustness. Simultaneously, expanding the spectral peak range makes it easier to obtain useful spectral peak information during spatial spectrum sampling. To implement the power spectrum blurring operation, covariance matrix sharpening is performed using an MZ sharpening matrix.

[0026] The MZ sharpening matrix is ​​defined as follows:

[0027] in, The fuzzy range parameter determines the range of fuzziness. The larger the value, the larger the blurring range of the spectral peak. The selected blurring range parameter should ensure that the blurred spectral peak can be observed by at least one observation point. The blurring range parameter selected in this embodiment of the invention is: M is the number of array elements.

[0028] Constructing the MZ sharpening matrix Then, the sampling covariance matrix is ​​subjected to spatial spectral fuzzification, which is expressed as:

[0029] After completing the spatial spectral fuzzification, a sampling matrix is ​​established, and the matrix is ​​then processed. Perform spatial spectrum sampling operation.

[0030] Construct the sampling matrix:

[0031] in, The steering vector represents the direction from which the signal originates. The phase relationship to the array, It is the ambiguous region of the expected signal direction. In the angle range The supplement to the above.

[0032] After sampling the spatial spectrum, we obtain:

[0033] Use the MZ sharpening matrix again The sampling results are then blurred to obtain the reconstructed interference plus noise covariance matrix, i.e.:

[0034] 2) Perform minimum variance distortionless response beamforming After reconstructing the interference plus noise covariance matrix, minimum variance distortionless response optimization can be performed:

[0035]

[0036] Construct the Lagrange function using the Lagrange multiplier method:

[0037] The array weighting vector is obtained by solving:

[0038] Beamforming is performed based on array weighted vectors to obtain beam domain data.

[0039] 3) Continuously conduct simulations After executing the anti-jamming beamforming algorithm, the beam pattern data continues to be sent to the display and control component and the target tracking component. At this time, the target feature lines can be clearly observed on the human-machine interface, and interference is significantly suppressed. Simultaneously, the target tracking component performs Kalman filtering to estimate the target's true azimuth. ,Will The target tracking component sends the desired signal arrival angle to the beamforming component via a data interface, which is then fed back to the anti-jamming beamforming component. This serves as the desired signal arrival angle required for processing the next batch of array element domain data. By repeating the above steps, joint simulation of anti-jamming beamforming and target tracking can be performed on potential targets at the approximate azimuth of the target initially selected by the operator. The operator can flexibly switch between the anti-jamming beamforming algorithm and the conventional beamforming algorithm as needed, without interrupting the simulation process. This approach balances multi-target (including interference) and anti-jamming beamforming for single potential targets, improving simulation efficiency.

[0040] In another embodiment of the invention, such as Figure 2 As shown, the co-simulation system is first divided into four modules: array data generation component, beamforming component, display and control component, and target tracking component. The array data generation component is preset to a linear array with 72 channels. The environmental parameters are: Munk sound velocity profile, flat terrain, seabed sediment of silt and sand, and sea depth of 2000 meters. The target parameters are: target radiated noise level 130 dB, depth 10 m, distance 5 km, and azimuth 65°; interference radiated noise 135 dB, depth 2 m, distance 5 km, and azimuth 138°. 2. Define standardized control and data interfaces; 3. Determine the ambiguity interval of the direction of arrival of the desired signal. The fuzzy range is ±3°. 4. Run the simulation program and execute the conventional beamforming algorithm normally; 5. The operator observes the results of the conventional beamforming algorithm through the human-machine interface; 6. The operator selects to trigger the anti-interference beamforming algorithm as needed, and the platform records the potential target direction at 63° at this time; 7. When performing beamforming on the next batch of data, the platform's beamforming component switches to an anti-interference algorithm to perform spatial power spectrum sampling and interference covariance reconstruction. It calculates the sampling covariance matrix for the input data, scans the omnidirectional space, and calculates the power spectrum in each direction. Based on the potential target direction and ambiguity range, it determines the ambiguity interval for the direction of arrival of the desired signal. The range is [60°, 66°]. The spatial power spectrum outside this interval is weighted and integrated to reconstruct the interference + noise covariance matrix. 8. Calculate the optimal beamforming weight vector using the MVDR criterion and perform beamforming; 9. Execute target tracking; the platform records the updated potential target direction at 63°. 10. Repeat steps 7-9, or switch between the anti-jamming beamforming algorithm and the conventional beamforming algorithm as needed by the operator. The simulation remains uninterrupted throughout the process. The human-machine interface displays the differences in time-azimuth history before and after executing the anti-jamming beamforming algorithm. Figure 3 As shown.

[0041] The specific embodiments of this intellectual property have been described above and should not be construed as limiting the scope of this intellectual property. Those skilled in the art can make equivalent substitutions, designs, or improvements to the algorithm details, simulation platform, component interfaces, beamforming algorithm switching / triggering methods, etc., within the scope of the claims, without affecting the substantive content of this intellectual property.

[0042] The foregoing has provided a detailed description of a co-simulation method and system for anti-interference beamforming and target tracking based on an open architecture, as provided in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A joint simulation method for anti-jamming beamforming and target tracking based on a modular open architecture, characterized in that, include: The beamforming and target tracking co-simulation system is decomposed into four functionally independent core modules: array data generation component, beamforming component, display and control component, and target tracking component; Standardized data and control interfaces are defined between modules, including: a data interface for transmitting 3D sound field data packets from the array data generation component to the beamforming component; a data interface for transmitting beam pattern data from the beamforming component to the display and control component and the target tracking component; a data interface for transmitting the desired signal wave angle estimate from the target tracking component to the beamforming component; a control interface for transmitting tracking commands and the initial azimuth of the target from the display and control component to the target tracking component; and a data interface for transmitting target trajectory information from the target tracking component to the display and control component. The array element-level acoustic signal data is generated by the array element data generation component, the beamforming component generates beam domain data using a conventional beamforming algorithm, the display and control component displays the time-azimuth history map in real time, and the operator selects a potential target and triggers the target tracking component to perform target tracking. When the operator triggers the switching of the anti-interference beamforming algorithm through the display and control components, the current data batch number is recorded. After the beamforming component completes the processing of the current batch of data, it replaces the algorithm instance from the regular beamforming algorithm to the anti-interference beamforming algorithm in situ. The anti-interference beamforming algorithm is used for processing from the next batch of data onwards. Other modules do not run interrupted during the switching process. The anti-jamming beamforming algorithm reconstructs the interference plus noise covariance matrix based on the expected signal angle of arrival estimated by the target tracking component, solves the MVDR array weighting vector, and performs beamforming. The target tracking component updates the target state estimate based on the beammap data and feeds back the updated expected signal angle of arrival estimate to the beamforming component, forming a closed loop.

2. The method according to claim 1, characterized in that, The process of replacing the conventional beamforming algorithm with an anti-jamming beamforming algorithm in situ, and using the anti-jamming beamforming algorithm for processing from the next batch of data onwards, without interrupting the operation of other modules during the switching process, includes: The beamforming component internally uses a strategy pattern to manage multiple beamforming algorithm instances, while exposing a unified algorithm call interface to the outside world; When an algorithm switching instruction is received, the beamforming component switches the internally held algorithm instance pointer after the current data batch is processed, and calls the processing function of the new algorithm instance from the next batch of data. During the switching process, the input data buffer, output data queue, and interface connections with other modules of the beamforming component remain unchanged.

3. The method according to claim 2, characterized in that, The anti-interference beamforming algorithm reconstructs the interference plus noise covariance matrix based on the expected signal angle of arrival estimate fed back by the target tracking component, solves the MVDR array weighting vector, and performs beamforming. The target tracking component updates the target state estimate based on the beammap data and feeds back the updated expected signal angle of arrival estimate to the beamforming component, forming a closed loop, including: Constructing the MZ sharpening matrix For the sampling covariance matrix Spatial spectral fuzzification is performed to obtain ; Based on the ambiguity of the direction of arrival of the desired signal supplement Constructing the sampling matrix ,right Spatial spectrum sampling was performed to obtain ; The sampling results are blurred again using the MZ sharpening matrix to obtain the reconstructed interference plus noise covariance matrix. ; Based on the reconstructed interference plus noise covariance matrix and desired signal steering vector Solve for the array weighting vector: ; The beam domain data is output by weighting and summing the array weighted vectors based on the array weighted vectors.

4. The method according to claim 3, characterized in that, The MZ sharpening matrix is ​​defined as follows: ,in, For the number of array elements, The fuzzy range parameter has a value of [value]. .

5. The method according to claim 4, characterized in that, The ambiguity interval of the direction of arrival of the desired signal The operator initially selects the direction. and preset fuzzy range Determined, that is ,in, The range of values ​​is to .

6. The method according to claim 5, characterized in that, The target tracking component uses the Kalman filter algorithm to achieve multi-target state estimation and outputs target trajectory parameters, including estimated values ​​of position, velocity, heading, and azimuth.

7. A joint simulation system for anti-jamming beamforming and target tracking based on a modular open architecture, characterized in that, include: The array data generation component is used to generate array element-level acoustic signal data that conforms to physical characteristics based on preset array parameters, environmental parameters and target parameters, and output three-dimensional sound field data packets in batches. The beamforming component, connected to the array data generation component, is used to receive three-dimensional sound field data packets, perform beamforming processing according to the currently active beamforming algorithm, and output beammap data. The beamforming component internally uses a strategy mode to manage at least two beamforming algorithm instances, including conventional beamforming algorithms and anti-interference beamforming algorithms, and supports dynamic switching at runtime. The display and control components are connected to the beamforming components and the target tracking components, respectively. They are used to display the beam pattern and target trajectory in real time, and provide a human-machine interface for operators to select targets, enable or disable tracking, and trigger beamforming algorithm switching. The target tracking component is connected to the beamforming component and the display and control component respectively. It is used to perform target state estimation based on beam pattern data and tracking commands, combined with the target motion model, output target trajectory parameters, and feed back the estimated value of the expected signal arrival angle to the beamforming component. The components communicate with each other through standardized data and control interfaces, enabling the beamforming component to perform in-situ algorithm replacement during simulation without interrupting the operation of other components.

8. The system according to claim 7, characterized in that, When the beamforming component receives an algorithm switching instruction, it switches the internally held algorithm instance pointer after the current data batch is processed, and calls the processing function of the new algorithm instance from the next batch of data. During the switching process, the interface connection with other components remains unchanged.

9. The system according to claim 8, characterized in that, The anti-interference beamforming algorithm is an interference plus noise covariance reconstruction MVDR beamforming algorithm based on spatial spectrum sampling, including: The MZ sharpening matrix construction module is used to perform spatial spectral blurring on the sampling covariance matrix. The spatial spectrum sampling module is used to construct a sampling matrix based on the complement of the ambiguous interval of the direction of arrival of the desired signal and to perform spatial spectrum sampling. The MVDR weighting module is used to solve for the array weighting vector based on the reconstructed interference plus noise covariance matrix; The beamforming execution module is used to output beam domain data based on the array weighted vector.

10. The system according to claim 9, characterized in that, The standardized data interface includes: The first data interface for the array data generation component to transmit three-dimensional sound field data packets to the beamforming component; A second data interface for the beamforming component to transmit beam pattern data to the display and control component; The third data interface for the beamforming component to transmit beam pattern data to the target tracking component; The target tracking component transmits the desired signal to the beamforming component via a fourth data interface that provides the estimated wave angle. The display and control component transmits tracking commands and the initial orientation of the target to the target tracking component via a control interface. The fifth data interface through which the target tracking component transmits target trajectory information to the display and control component.

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