Multi-period four-dimensional plot collaborative association method for distributed phased array radar networking

Abstract

The invention relates to the technical field of radar signal processing, and discloses a multi-cycle four-dimensional plot collaborative association method for distributed phased array radar networking, which comprises the following steps: acquiring multi-cycle four-dimensional plot data and a priori space constraint model, screening a candidate plot set based on a space-time constraint matrix, constructing a plot association map and giving a weight, and generating a global optimal association path set by using an improved multi-target optimization algorithm, and mapping the global optimal association path set to the target trajectory model to complete collaborative association. The method can reduce the data volume through space-time constraint, improves the calculation efficiency, combines the advantages of a genetic algorithm and a particle swarm optimization algorithm, improves the robustness and correlation accuracy, is suitable for a low-signal-to-noise-ratio scene, and remarkably improves the overall tracking performance.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, specifically to a multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking. Background Technology

[0002] With the development of radar technology, distributed phased array radar networks have demonstrated higher accuracy and efficiency in target detection and tracking. In practical applications, the collaborative association of multi-period four-dimensional point data is a crucial step in achieving efficient target tracking. However, existing technologies often rely on complex computational processes to ensure the accuracy of the association when processing multi-period point data, which increases the computational burden and time cost of the system to some extent.

[0003] Currently, a common solution to improve data processing efficiency is to optimize algorithm structure and reduce unnecessary computational steps, thereby shortening processing time. However, this method may have certain limitations in practical applications, such as a decrease in association performance for some low signal-to-noise ratio targets, thus affecting the overall tracking effect. Therefore, to solve the above-mentioned problems, a more efficient multi-period four-dimensional point trace collaborative association method is needed. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking, and a multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices. One or more embodiments of the present invention also relate to a multi-period four-dimensional point trace collaborative association device for distributed phased array radar networking, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the present invention, a multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking is provided, comprising: Acquire a set of multi-period four-dimensional point trace data collected by multiple distributed phased array radar nodes for the target area, as well as the prior space constraint model corresponding to the target area; Based on the spatiotemporal constraint matrix in the prior spatial constraint model, the multi-period four-dimensional point trace data set is initially screened to determine the candidate point trace set that satisfies spatiotemporal consistency in each period. The spatiotemporal constraint matrix is ​​used to record the mapping relationship between the spatial distribution characteristics and the temporal dynamic characteristics within the target area. Based on the position information and motion state information of the candidate point traces in the set, a point trace association graph is constructed between each cycle. The point trace association graph represents points through nodes, edges represent the association strength between points, and each edge is assigned a weight based on a preset weight allocation rule. An improved multi-objective optimization algorithm is used to process the point-track association map to generate a globally optimal set of point-track association paths. The improved multi-objective optimization algorithm combines the advantages of genetic algorithm and particle swarm optimization algorithm, and balances global search capability and local convergence accuracy by adaptively adjusting parameters. The globally optimal set of point-track association paths is mapped to the target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data.

[0006] According to a second aspect of the present invention, a multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices is provided, comprising: Receive a point-track collaborative association instruction for a target area, wherein the point-track collaborative association instruction is sent by a terminal device, and the point-track collaborative association instruction includes a multi-period four-dimensional point-track data set collected by multiple distributed phased array radar nodes and a priori spatial constraint model corresponding to the target area; Based on the spatiotemporal constraint matrix in the prior spatial constraint model, the multi-period four-dimensional point trace data set is initially screened to determine the candidate point trace set that satisfies spatiotemporal consistency in each period. The spatiotemporal constraint matrix is ​​used to record the mapping relationship between the spatial distribution characteristics and the temporal dynamic characteristics within the target area. Based on the position information and motion state information of the candidate point traces in the set, a point trace association graph is constructed between each cycle. The point trace association graph represents points through nodes, edges represent the association strength between points, and each edge is assigned a weight based on a preset weight allocation rule. An improved multi-objective optimization algorithm is used to process the point-track association map to generate a globally optimal set of point-track association paths. The improved multi-objective optimization algorithm combines the advantages of genetic algorithm and particle swarm optimization algorithm, and balances global search capability and local convergence accuracy by adaptively adjusting parameters. The globally optimal set of point-track association paths is mapped to the target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data, and the collaborative association result is returned to the terminal device.

[0007] According to a third aspect of the present invention, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the above-mentioned multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking and the multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices.

[0008] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores computer-executable instructions that, when executed by a processor, implement the steps of the above-described multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking and the multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices.

[0009] According to a fifth aspect of the present invention, a computer program product is provided, including a computer program / instruction that, when executed by a processor, implements the steps of the above-described multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking and the multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices.

[0010] One embodiment of the present invention acquires a multi-period four-dimensional point trace data set collected by multiple distributed phased array radar nodes targeting a target area, and a priori spatial constraint model corresponding to the target area; based on the spatiotemporal constraint matrix in the priori spatial constraint model, the multi-period four-dimensional point trace data set is initially screened to determine a candidate point trace set that satisfies spatiotemporal consistency within each period, wherein the spatiotemporal constraint matrix is ​​used to record the mapping relationship between spatial distribution characteristics and temporal dynamic characteristics within the target area; based on the point trace position information and motion state information in the candidate point trace set, an inter-period... A point-track association graph is generated, wherein nodes represent points, edges represent the association strength between points, and each edge is assigned a weight based on a preset weight allocation rule. An improved multi-objective optimization algorithm is used to process the point-track association graph to generate a globally optimal set of point-track association paths. This improved multi-objective optimization algorithm combines the advantages of genetic algorithms and particle swarm optimization algorithms, adaptively adjusting parameters to balance global search capability and local convergence accuracy. The globally optimal set of point-track association paths is then mapped onto a target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data.

[0011] By applying the technical solution of this invention, a spatiotemporal constraint matrix from a priori spatial constraint model is introduced to initially screen multi-period four-dimensional point data, reducing the amount of data processed subsequently and thus lowering the computational burden on the system. Based on this, by constructing a point association map and employing an improved multi-objective optimization algorithm, computational efficiency can be significantly improved while ensuring association accuracy. Furthermore, the improved multi-objective optimization algorithm, through adaptive parameter adjustment, exhibits stronger robustness in complex scenarios and is suitable for the efficient association requirements of low signal-to-noise ratio targets, thereby improving overall tracking performance. Attached Figure Description

[0012] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the preliminary screening process based on the spatiotemporal constraint matrix of this invention; Figure 3 This is a flowchart of the point association map construction process of the present invention; Figure 4 The flowchart of the improved multi-objective optimization algorithm of this invention is shown below; Figure 5 This is a flowchart of the target trajectory model mapping process of the present invention; Figure 6 This is a flowchart illustrating the cloud-based device collaboration and association process of the present invention. Detailed Implementation

[0013] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] This invention provides a multi-period four-dimensional point trace collaborative association method and related apparatus for distributed phased array radar networks. The specific implementation of this invention is described in detail below with reference to the accompanying drawings. In this embodiment, the target area is covered by multiple distributed phased array radar nodes. These nodes track and identify targets within the target area by collecting multi-period four-dimensional point trace data sets. A priori spatial constraint model, as one of the core components, guides the entire point trace data processing flow. It contains a spatiotemporal constraint matrix to record the mapping relationship between the spatial distribution characteristics and temporal dynamic characteristics within the target area. Through the collaborative work of the above components, this invention achieves efficient and accurate multi-period four-dimensional point trace collaborative association.

[0015] like Figure 1As shown, the overall process of this embodiment includes acquiring a multi-period four-dimensional point trace dataset, merging it with a spatiotemporal constraint matrix based on a priori spatial constraint model to perform preliminary screening of the data, determining a candidate point trace set, then constructing a point trace association map and generating a globally optimal point trace association path set through an improved multi-objective optimization algorithm, and finally mapping this path set to a target trajectory model to complete collaborative association. Specifically, it is first necessary to acquire a multi-period four-dimensional point trace data set from distributed phased array radar nodes. This data set contains the position and motion state information of targets within the target area at different time periods. To ensure the efficiency and accuracy of subsequent processing, a priori spatial constraint model is introduced for preliminary screening of the data set. Figure 2 As shown, the screening process is based on a spatiotemporal constraint matrix. This matrix verifies the spatiotemporal consistency of each point in the dataset through a pre-defined mapping relationship between spatial distribution characteristics and temporal dynamic characteristics, thereby eliminating points that do not meet the conditions and retaining a set of candidate points that meet the requirements. This process significantly reduces the amount of data required for subsequent calculations, laying the foundation for improving the overall performance of the system.

[0016] After determining the candidate point set, the next step is to construct a point association map based on point location information and motion state information, such as... Figure 3 As shown, a point-track association graph is a logical structure where each node represents a point track, and each edge represents the association strength between points tracks. To quantify this association strength, this embodiment uses a preset weighting rule to assign weights to each edge. The weighting rule comprehensively evaluates parameters such as distance, velocity difference, and time interval between points tracks to ensure that the weights accurately reflect the degree of association between them. For example, when two points tracks are spatially close and have similar motion states, the edge between them will be assigned a higher weight, and vice versa. In this way, the point-track association graph can comprehensively reflect the potential association relationships between points in the candidate point track set, providing a reliable basis for the execution of subsequent optimization algorithms.

[0017] Next, an improved multi-objective optimization algorithm is used to process the point association map to generate a globally optimal set of point association paths. For example... Figure 4As shown, the improved multi-objective optimization algorithm combines the advantages of genetic algorithms and particle swarm optimization algorithms, balancing global search capability and local convergence accuracy through adaptive parameter adjustment. Specifically, the genetic algorithm is responsible for a large-scale search across the entire solution space, ensuring that no potential optimal solutions are missed; while the particle swarm optimization algorithm focuses on local searches, quickly converging to the vicinity of the optimal solution. The two algorithms work together by dynamically adjusting parameters such as crossover probability, mutation probability, and the inertia weight of the particle swarm. For example, in the initial stage, the algorithm tends to expand the search range to explore more possibilities, while in the later stages it gradually narrows the search range to improve convergence accuracy. In this way, the improved multi-objective optimization algorithm exhibits stronger robustness in complex scenarios and is suitable for the efficient association requirements of low signal-to-noise ratio targets.

[0018] After generating the globally optimal set of point-track association paths, these paths are mapped onto the target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data. For example... Figure 5 As shown, the target trajectory model is a mathematical model used to describe the motion trajectory of a target, and its input is the path information in the set of associated paths of point traces. By mapping each path in the path set sequentially to the target trajectory model, the complete motion trajectory of each target within the target area can be obtained. This process not only realizes efficient collaborative association of multi-period four-dimensional point trace data, but also provides a reliable basis for subsequent target tracking and identification.

[0019] Furthermore, this embodiment also provides a multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices, such as... Figure 6 As shown in the diagram. In this method, the terminal device sends a point-track collaborative association instruction to the cloud device. This instruction includes a multi-period four-dimensional point-track data set and a priori spatial constraint model. After receiving the instruction, the cloud device sequentially executes the aforementioned steps: preliminary screening based on the spatiotemporal constraint matrix, construction of the point-track association map, processing using an improved multi-objective optimization algorithm, and generation of a set of globally optimal point-track association paths. Finally, the cloud device returns the collaborative association results to the terminal device, completing the closed-loop operation of the entire process. During this process, the high-performance computing capability of the cloud device significantly improves data processing efficiency, especially in large-scale distributed phased array radar networking scenarios.

[0020] In practical applications, the method of this embodiment can be widely used in fields such as military reconnaissance, air traffic control, and natural disaster monitoring. For example, in military reconnaissance scenarios, multiple distributed phased array radar nodes are deployed around the target area. By collecting multi-period four-dimensional point data sets in real time and uploading them to cloud devices, the target's motion trajectory can be quickly generated, providing support for command and decision-making. In air traffic control scenarios, this method can effectively address the target tracking needs in high-density aircraft environments, ensuring flight safety. In natural disaster monitoring scenarios, by analyzing point data on geological activity or meteorological changes within the target area, potential risks can be identified in a timely manner, and corresponding measures can be taken.

[0021] To further illustrate the technical details of this embodiment, a detailed explanation is provided below with specific examples. Assume the target area is an airspace of 100 square kilometers, with 5 distributed phased array radar nodes deployed. Each node collects four-dimensional point data every 1 second. After 10 cycles of data collection, a total of 5000 point data points are obtained, forming a multi-cycle four-dimensional point data set. Based on the spatiotemporal constraint matrix in the prior spatial constraint model, these 5000 point data points are initially screened, removing those that do not conform to spatiotemporal consistency, retaining 1000 point data points as a candidate point set. Subsequently, based on the point location information and motion state information in the candidate point set, a point association graph is constructed, containing 1000 nodes and approximately 5000 edges. The weight of each edge is calculated according to a preset weight allocation rule. For example, if the distance between two points connected by an edge is 100 meters, the speed difference is 5 meters per second, and the time interval is 1 second, then the weight of that edge is calculated to be 0.8. An improved multi-objective optimization algorithm is used to process the point trace association map, ultimately generating a set of globally optimal point trace association paths containing 200 paths. These paths are then mapped onto the target trajectory model to obtain the complete motion trajectories of 200 targets within the target area.

[0022] In terms of hardware implementation, this embodiment also includes a computing device comprising a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the various steps of the above-described method. Furthermore, this embodiment also relates to a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, can also implement the various steps of the above-described method. Finally, this embodiment also provides a computer program product comprising a computer program or instructions, which, when executed by a processor, can implement the various steps of the above-described method.

[0023] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0024] In a military reconnaissance scenario, the target area is an airspace of 100 square kilometers, where five distributed phased array radar nodes are deployed. Each node collects four-dimensional point data every second. After 10 data acquisition cycles, a total of 5000 point data points are obtained, forming a multi-cycle four-dimensional point data set. This data contains information on the target's position and motion status at different time periods. To ensure the efficiency and accuracy of subsequent processing, a spatiotemporal constraint matrix from the prior spatial constraint model is introduced to perform preliminary screening of the multi-cycle four-dimensional point data set. For example... Figure 2 As shown, the spatiotemporal constraint matrix verifies the spatiotemporal consistency of the point data one by one based on the preset mapping relationship between spatial distribution characteristics and temporal dynamic characteristics, eliminates points that do not meet the conditions, and finally retains 1000 points as a candidate point set. This screening process significantly reduces the amount of data for subsequent calculations, thereby reducing the computational burden of the system.

[0025] After determining the candidate point set, the next step is to construct a point association map based on the point location information and motion state information. For example... Figure 3 As shown, the trace association graph is a logical structure where each node represents a trace, and each edge represents the association strength between traces. To quantify this association strength, a preset weighting rule is used to assign a weight to each edge. For example, when the distance between two traces is 100 meters, the speed difference is 5 meters per second, and the time interval is 1 second, the weight of the edge is calculated to be 0.8. In this way, the trace association graph can comprehensively reflect the potential association relationships between traces in the candidate trace set, providing a reliable basis for the execution of subsequent optimization algorithms.

[0026] Subsequently, an improved multi-objective optimization algorithm is used to process the point-track association map to generate a globally optimal set of point-track association paths. For example... Figure 4 As shown, the improved multi-objective optimization algorithm combines the advantages of genetic algorithms and particle swarm optimization algorithms. In the initial stage, the genetic algorithm is responsible for a large-scale search across the entire solution space, ensuring that no potential optimal solutions are missed; while the particle swarm optimization algorithm focuses on local searches, quickly converging to the vicinity of the optimal solution. The two algorithms work together by dynamically adjusting parameters such as crossover probability, mutation probability, and the inertia weight of the particle swarm. For example, in the early stages of the search, the algorithm tends to expand the search range to explore more possibilities, while in the later stages it gradually narrows the search range to improve convergence accuracy. In this way, the improved multi-objective optimization algorithm exhibits stronger robustness in complex scenarios and is suitable for the efficient association requirements of low signal-to-noise ratio targets.

[0027] After generating the globally optimal set of point-track association paths, these paths are mapped onto the target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data. For example... Figure 5As shown, the target trajectory model is a mathematical model used to describe the motion trajectory of a target, and its input is the path information in the set of associated paths of point traces. By mapping each path in the path set sequentially to the target trajectory model, the complete motion trajectory of each target within the target area can be obtained. This process not only realizes efficient collaborative association of multi-period four-dimensional point trace data, but also provides a reliable basis for subsequent target tracking and identification.

[0028] Furthermore, the method of this embodiment can be applied to cloud devices for multi-period four-dimensional point trace collaborative association in distributed phased array radar networking. For example... Figure 6 As shown, the terminal device sends a point-track collaborative association command to the cloud device. This command includes a multi-period four-dimensional point-track data set and a priori spatial constraint model. Upon receiving the command, the cloud device sequentially executes the aforementioned steps: preliminary screening based on the spatiotemporal constraint matrix, construction of the point-track association map, processing using an improved multi-objective optimization algorithm, and generation of the global optimal point-track association path set. Finally, the cloud device returns the collaborative association results to the terminal device, completing the closed-loop operation of the entire process. During this process, the high-performance computing capabilities of the cloud device significantly improve data processing efficiency, particularly excelling in large-scale distributed phased array radar networking scenarios.

[0029] Through the above steps, the technical solution of this invention can effectively solve the problems existing in the prior art. First, by introducing the spatiotemporal constraint matrix in the prior spatial constraint model to perform preliminary screening of multi-period four-dimensional point data, the amount of data processed subsequently is reduced, thereby reducing the computational burden of the system. Second, by constructing a point association map and using an improved multi-objective optimization algorithm for processing, computational efficiency can be significantly improved while ensuring association accuracy. Finally, the improved multi-objective optimization algorithm, through adaptive parameter adjustment, exhibits stronger robustness in complex scenarios and is suitable for the efficient association requirements of low signal-to-noise ratio targets, thereby improving overall tracking performance.

[0030] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking, characterized in that, include: Acquire a set of multi-period four-dimensional point trace data collected by multiple distributed phased array radar nodes for the target area, as well as the prior space constraint model corresponding to the target area; Based on the spatiotemporal constraint matrix in the prior spatial constraint model, the multi-period four-dimensional point trace data set is initially screened to determine the candidate point trace set that satisfies spatiotemporal consistency in each period. The spatiotemporal constraint matrix is ​​used to record the mapping relationship between the spatial distribution characteristics and the temporal dynamic characteristics within the target area. Based on the position information and motion state information of the candidate point traces in the set, a point trace association graph is constructed between each cycle. The point trace association graph represents points through nodes, edges represent the association strength between points, and each edge is assigned a weight based on a preset weight allocation rule. An improved multi-objective optimization algorithm is used to process the point-track association map to generate a globally optimal set of point-track association paths. The improved multi-objective optimization algorithm combines the advantages of genetic algorithm and particle swarm optimization algorithm, and balances global search capability and local convergence accuracy by adaptively adjusting parameters. The globally optimal set of point-track association paths is mapped to the target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data.

2. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 1, characterized in that, Based on the spatiotemporal constraint matrix in the prior spatial constraint model, the multi-period four-dimensional point trace data set is initially screened, including: Extract the spatial coordinates and timestamp information of each point from the multi-period four-dimensional point data set; The spatial coordinates and timestamp information are matched with the preset mapping relationship in the spatiotemporal constraint matrix, and the points that do not conform to spatiotemporal consistency are eliminated, while the points that meet the conditions are retained as a candidate point set.

3. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 1, characterized in that, Constructing a point-track correlation map between different periods, including: Determine the position and motion state information of each point in the candidate point set; Calculate the distance, velocity difference, and time interval between every two points; Based on the distance, speed difference, and time interval, each edge is assigned a weight according to a preset weight allocation rule to form a point-track association graph.

4. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 3, characterized in that, The preset weight allocation rules include: A first weighting coefficient is set for distance, a second weighting coefficient is set for speed difference, and a third weighting coefficient is set for time interval; The weight of each edge is calculated based on the weighted sum of the first weight coefficient, the second weight coefficient, and the third weight coefficient.

5. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 1, characterized in that, The point-track association map is processed using an improved multi-objective optimization algorithm, including: Initialize the population, where each individual represents a possible path of connection between points; New individuals are generated by performing a global search of the population using a genetic algorithm. The particle swarm optimization algorithm is used to perform local search on the population to improve convergence accuracy. The fitness function is used to evaluate the quality of each individual, and the best individual is selected as the globally optimal set of point association paths.

6. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 5, characterized in that, The improved multi-objective optimization algorithm balances global search capability and local convergence accuracy by adaptively adjusting parameters, including: Increase the crossover and mutation probabilities in the initial stage to broaden the search scope; In the later stages, the crossover and mutation probabilities are reduced to improve convergence accuracy.

7. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 1, characterized in that, Mapping the globally optimal set of associated paths to the target trajectory model includes: Extract the point sequence of each path from the globally optimal point-related path set; The point sequence is input into the target trajectory model to generate the complete motion trajectory of the target within the target area.

8. A multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking applied to cloud devices, characterized in that, include: Receive a point-track collaborative association instruction for a target area, wherein the point-track collaborative association instruction is sent by a terminal device, and the point-track collaborative association instruction includes a multi-period four-dimensional point-track data set collected by multiple distributed phased array radar nodes and a priori spatial constraint model corresponding to the target area; Based on the spatiotemporal constraint matrix in the prior spatial constraint model, the multi-period four-dimensional point trace data set is initially screened to determine the candidate point trace set that satisfies spatiotemporal consistency in each period. Based on the position and motion state information of the candidate point traces in the set of candidate point traces, a point trace association map is constructed for each cycle; An improved multi-objective optimization algorithm is used to process the point-track association map to generate a globally optimal set of point-track association paths; The globally optimal set of point-track association paths is mapped to the target trajectory model to complete the collaborative association of multi-period four-dimensional point-track data, and the collaborative association result is returned to the terminal device.

9. The multi-period four-dimensional point trace collaborative association method for distributed phased array radar networking according to claim 8, characterized in that, The point-track collaborative association instruction also includes the geographic boundary information and time range information of the target area, which are used to assist in the filtering process of the spatiotemporal constraint matrix. The cloud device executes the preliminary screening, point association map construction, improved multi-objective optimization algorithm processing, and target trajectory model mapping steps through high-performance computing resources.

10. A multi-period four-dimensional point track collaborative correlation device for distributed phased array radar networking, characterized in that, include: The data acquisition module is used to acquire multi-period four-dimensional point trace data sets collected by multiple distributed phased array radar nodes and the prior space constraint model corresponding to the target area; The preliminary screening module is used to perform preliminary screening on the multi-period four-dimensional point trace data set based on the spatiotemporal constraint matrix in the prior spatial constraint model to determine the candidate point trace set. The map construction module is used to construct a map association based on the location information and motion state information of the candidate map points in the set of candidate map points. The optimization processing module is used to process the point association map using an improved multi-objective optimization algorithm to generate a globally optimal set of point association paths; The trajectory mapping module is used to map the globally optimal set of point-track association paths to the target trajectory model, thereby completing the collaborative association of multi-period four-dimensional point-track data.

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