DG-MFCO-based deception source optimization deployment method, device and equipment

Abstract

The invention relates to a deception source optimization deployment method, device and equipment based on DG-MFCO. The method comprises the following steps: acquiring a digital elevation model of a target area, and acquiring a ground candidate point set from the digital elevation model; performing vertical stratified sampling on the airspace of the target area according to a gridding method, and generating a three-dimensional airspace point set containing elevation data; based on a digital elevation model, constructing a terrain occlusion perception visual field model by adopting a reference plane algorithm, judging the visibility of ground candidate points and three-dimensional airspace points through a ray tracing principle, and generating a three-dimensional sight line mask matrix; generating a coverage vector of each ground candidate point according to the three-dimensional sight line mask matrix; the coverage vector comprises a coverage rate; and by taking coverage rate maximization as a target, dynamically updating the priorities of the ground candidate points by adopting a two-stage optimization strategy, selecting the ground candidate point with the highest potential value, adding the ground candidate point into a deployment point set, and outputting a deception source coordinate vector. By adopting the method, the cheating source coverage rate can be maximized.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a method, apparatus and device for optimizing the deployment of deception sources based on DG-MFCO. Background Art

[0002] With the rapid evolution of modern technology, positioning countermeasures have become a critical component of complex system dynamics. Satellite positioning jamming, an emerging technology in the field, has sparked extensive research since its conception. Satellite signals are extremely weak. For example, the strength of a Global Positioning System (GPS) signal reaching a receiving terminal is approximately -130dBm. Therefore, the propagation process is highly susceptible to environmental factors.

[0003] However, in complex geographic scenarios, traditional signal source deployment methods rely on empirical site selection strategies, making it difficult to accurately quantify the actual impact of terrain obstruction on signal transmission, resulting in significant uncertainty in coverage effectiveness. Furthermore, existing solutions lack the ability to dynamically adapt to signal sources of varying sizes. When the number of signal sources is small, global search algorithms struggle to meet real-time requirements due to their high computational complexity. In scenarios where multiple signal sources are deployed collaboratively, heuristic algorithms are prone to falling into local optimal solutions, resulting in inefficient resource allocation or overlapping and redundant coverage areas. Summary of the Invention

[0004] Based on this, it is necessary to provide a method that can maximize the coverage of deception sources in response to the above technical problems. A deception source optimization deployment method, device and equipment based on DG-MFCO.

[0005] A deception source optimization deployment method based on DG-MFCO, the method comprising: Obtain a digital elevation model of the target area and obtain a set of ground candidate points from the digital elevation model; perform vertical layered sampling of the airspace of the target area using a gridding method to generate a three-dimensional airspace point set containing elevation data; Based on the digital elevation model, a reference surface algorithm is used to construct a terrain occlusion-aware visual domain model. The visibility of candidate ground points and 3D airspace points is determined by ray tracing, and a 3D line of sight mask matrix is ​​generated. A coverage vector is generated for each ground candidate point based on the 3D line of sight mask matrix. The coverage vector includes the coverage rate. With the goal of maximizing the coverage rate, a two-stage optimization strategy is used to dynamically update the priority of the ground candidate points. The ground candidate points with the highest potential value are selected to be added to the deployment point set, and the deception source coordinate vector is output.

[0006] A deception source optimization deployment device based on DG-MFCO, the device comprising: The point set generation module is used to obtain a digital elevation model of the target area and obtain a set of ground candidate points from the digital elevation model; vertically layered sampling of the airspace of the target area is performed using a gridding method to generate a three-dimensional airspace point set containing elevation data; The mask matrix generation module is used to construct a terrain occlusion-aware visual domain model based on the digital elevation model using a reference surface algorithm. The visibility of candidate ground points and 3D airspace points is determined by ray tracing principles to generate a 3D line-of-sight mask matrix. The deception source optimization deployment module is used to generate a coverage vector for each ground candidate point based on the 3D line of sight mask matrix. The coverage vector includes the coverage rate. With the goal of maximizing the coverage rate, a two-stage optimization strategy is used to dynamically update the priority of the ground candidate points, select the ground candidate points with the highest potential value to add to the deployment point set, and output the deception source coordinate vector.

[0007] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Obtain a digital elevation model of the target area and obtain a set of ground candidate points from the digital elevation model; perform vertical layered sampling of the airspace of the target area using a gridding method to generate a three-dimensional airspace point set containing elevation data; Based on the digital elevation model, a reference surface algorithm is used to construct a terrain occlusion-aware visual domain model. The visibility of candidate ground points and 3D airspace points is determined by ray tracing, and a 3D line of sight mask matrix is ​​generated. A coverage vector is generated for each ground candidate point based on the 3D line of sight mask matrix. The coverage vector includes the coverage rate. With the goal of maximizing the coverage rate, a two-stage optimization strategy is used to dynamically update the priority of the ground candidate points. The ground candidate points with the highest potential value are selected to be added to the deployment point set, and the deception source coordinate vector is output.

[0008] The DG-MFCO-based method, apparatus, and device for optimizing the deployment of deception sources employs three-dimensional airspace modeling and signal propagation analysis to solve for the global optimal placement solution. The target area is first gridded, and ground grid points are sampled vertically at intervals and in layers to generate a three-dimensional airspace point set containing elevation data. Simultaneously, a terrain-occlusion-aware visual domain model is constructed based on ray tracing principles to quantify the signal coverage of candidate ground grid points on three-dimensional airspace points, resulting in a coverage matrix and coverage rate. An integer programming model is established with the goal of maximizing coverage, and the model solution employs a phased optimization strategy. Initially, a parallelized greedy algorithm is used to rapidly select points with the greatest coverage contribution. Later, a multi-stage neighborhood look-ahead mechanism is introduced to evaluate the potential of uncovered areas based on a distance-attenuation weighted function. The point with the greatest potential is selected as the optimal location for the deception source, providing a reproducible and optimizable technical path for the efficient deployment of satellite positioning jamming systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 FIG4 is a flow chart of a method for optimizing the deployment of deception sources based on DG-MFCO in one embodiment; Figure 2 FIG. 1 is a detailed flowchart of a method for optimizing the deployment of a deception source based on DG-MFCO in one embodiment; Figure 3 FIG1 is a structural block diagram of a deception source optimization deployment device based on DG-MFCO in one embodiment; Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0011] In one embodiment, Figure 1 As shown, a deception source optimization deployment method based on DG-MFCO is provided, including the following steps: Step 102: Obtain a digital elevation model of the target area, and obtain a ground candidate point set from the digital elevation model; perform vertical layered sampling on the airspace of the target area according to a gridding method to generate a three-dimensional airspace point set containing elevation data.

[0012] Using a digital elevation model (DEM) and 3D spatial stratified sampling technology, the target area's terrain is discretized into a computable spatial grid system. Ground nodes are divided into a 50m x 50m grid for a 10km x 10km area, and vertically, spatial nodes are generated at 20m intervals. This results in a 3D spatial model containing approximately 2 million voxels (200 x 200 x 50, assuming a 1000m elevation difference).

[0013] Step 104 : Based on the digital elevation model, a reference surface algorithm is used to construct a terrain occlusion-aware visual domain model. The visibility of candidate ground points and three-dimensional airspace points is determined by ray tracing principles to generate a three-dimensional sight line mask matrix.

[0014] Visibility analysis based on ray tracing and reference surface algorithms determines line of sight between candidate ground points and airspace nodes one by one, generating a line-of-sight mask matrix accurate to each spatial point. Compared to the crude "radius coverage estimation" model used in traditional empirical site selection, this technology quantifies the impact of terrain obstruction on signal transmission as a 0 / 1 binary mask and percentage coverage. This allows for intuitive visualization of coverage blind spots and overlapping areas through matrix operations (for example, a candidate point may have only 35% airspace coverage in mountainous areas, while it reaches 82% in plain areas). This provides a quantitative basis for deployment decision-making and avoids coverage efficiency losses exceeding 40% (based on simulation data) due to terrain misjudgment.

[0015] In step 106, a coverage vector is generated for each ground candidate point based on the three-dimensional line of sight mask matrix; the coverage vector includes the coverage rate; with the goal of maximizing the coverage rate, a two-stage optimization strategy is used to dynamically update the priority of the ground candidate points, select the ground candidate point with the highest potential value to add to the deployment point set, and output the deception source coordinate vector.

[0016] In scenarios with a small number of signal sources (e.g., 1-3), multi-threaded parallel computation of candidate point coverage increments allows the selection of the points with the greatest coverage contribution from a pool of 10^4 candidate points within seconds. The integer programming model and coverage maximization objective function employed in this phase ensure that initial point selection captures 70%-80% of the global potential coverage gain, meeting the real-time requirements of emergency deployment scenarios. For coordinated deployments of five or more signal sources, a distance-attenuation weighting function converts the spatial location of uncovered areas into a quantified potential value (e.g., near-field uncovered points are weighted as 1, while far-field coverage decays inversely proportional to the square of the distance). Through a multi-stage evaluation mechanism, when selecting the fourth signal source, the algorithm not only calculates the coverage gain of the current point but also proactively simulates its impact on the subsequent deployment of the fifth and sixth sources (e.g., preserving the potential value of unoccupied areas). Simulation results show that this mechanism can improve global coverage by 15%-20% for multi-source deployments while reducing coverage overlap by 25%, significantly improving resource utilization compared to traditional greedy algorithms.

[0017] At the same time, a phased optimization strategy is adopted. In phase 1, a parallel greedy algorithm is used to quickly screen the points with the greatest coverage contribution. In phase 2, a distance decay weighted function is introduced to dynamically adjust the potential weight (α) and height weight (β) to evaluate the potential value of the uncovered area. Finally, simulation experiments show that this method is significantly superior to the traditional greedy algorithm.

[0018] In the DG-MFCO-based method for optimizing the deployment of deception sources, this application solves the globally optimal placement scheme through three-dimensional airspace modeling and signal propagation analysis. The target area is first gridded, and ground grid points are sampled at intervals along the vertical direction to generate a three-dimensional airspace point set containing elevation data. Simultaneously, a terrain-occlusion-aware visual domain model is constructed based on ray tracing principles to quantify the signal coverage of candidate ground grid points on three-dimensional airspace points, resulting in a coverage matrix and coverage rate. An integer programming model is established with the goal of maximizing coverage, and the model solution employs a phased optimization strategy: initially, a parallelized greedy algorithm is used to rapidly select points with the greatest coverage contribution. Later, a multi-stage neighborhood look-ahead mechanism is introduced to evaluate the potential value of uncovered areas based on a distance-attenuation weighted function. The point with the greatest potential value is selected as the optimal location for the deception source, providing a reproducible and optimizable technical path for the efficient deployment of satellite positioning jamming systems.

[0019] In one embodiment, obtaining a ground candidate point set from a digital elevation model includes: A three-dimensional terrain grid containing plane coordinates and elevation values ​​is obtained from the digital elevation model. Outlier correction and missing value filling are performed on the DEM data. The ground area is gridded at a fixed resolution. Candidate points that meet the deployment conditions are screened to form a set of ground candidate points as alternative locations for subsequent deception source deployment.

[0020] In one embodiment, vertical layered sampling is performed on the airspace of the target area according to a gridding method to generate a three-dimensional airspace point set containing elevation data, including: The gridding method is used to perform vertical layered sampling of the airspace of the target area to generate a three-dimensional airspace point set covering 10m to 500m of the surface. , used to quantify the spatial coverage of the deceptive signal; each spatial point represents a stereo interference unit to be covered.

[0021] In one embodiment, a terrain occlusion-aware visual domain model is constructed based on a digital elevation model using a reference surface algorithm. The visibility of candidate ground points and three-dimensional airspace points is determined by ray tracing principles to generate a three-dimensional line of sight mask matrix, including: Based on the digital elevation model, a reference surface algorithm is used to construct a terrain-occlusion-aware visual domain model. A reference plane is constructed by calculating the position of the deception source and auxiliary grid points near the target point. The target point's mapped elevation on the reference plane is then calculated. If the target point's actual elevation is greater than or equal to the mapped elevation, it is considered visible; otherwise, it is invisible due to terrain occlusion. A correction for earth curvature is added to the calculation to eliminate horizontal plane deviation during long-distance transmission.

[0022] In one embodiment, the process of generating a three-dimensional line of sight mask matrix includes: The size of the mask matrix is ​​determined according to the number of ground candidate points and 3D spatial points. The mask matrix is , represents the number of candidate ground points, Represents the number of three-dimensional spatial points. The number of rows of the mask matrix is ​​the number of candidate ground points, and the number of columns is the number of spatial points. For each combination of a ground candidate point and a 3D airspace point, the matrix elements are filled in according to the calculated visibility results. If a ground candidate point is judged to be visible to a 3D airspace point, the corresponding element in the mask matrix is ​​assigned a value of 1; if not, it is assigned a value of 0. In this way, all combinations of ground candidate points and 3D airspace points are traversed to complete the generation of the entire 3D line of sight mask matrix.

[0023] In a specific embodiment, according to the three-dimensional line of sight mask matrix M vis , generate the coverage vector for each candidate point D i , initialize the covering matrix , coverage ,If the candidate point is visible to the airspace point and the distance is less than the effective interference radius of the spoofing source, the airspace point is marked as covered, otherwise it is not covered.

[0024] In one embodiment, with the goal of maximizing coverage, a two-stage optimization strategy is used to dynamically update the priority of ground candidate points, select the ground candidate points with the highest potential value to add to the deployment point set, and output the spoofing source coordinate vector, including: A two-stage optimization strategy is adopted. When the number of deception sources is not greater than a preset threshold, a parallel greedy algorithm is used to traverse all ground candidate points and select the points with the largest coverage to quickly form an initial coverage network. When the number of deception sources is greater than the preset threshold, a multi-stage neighborhood look-ahead mechanism is introduced to expand the neighborhood search centered on the deployed point. The covered area is filtered through a sliding window mask, and the potential value of the uncovered area is calculated in combination with a distance attenuation weighted function. The priority of the ground candidate points is dynamically updated, and the ground candidate point with the highest potential value is selected to join the deployed point set, and the deception source coordinate vector is output.

[0025] In a specific embodiment, Figure 2 As shown, based on coverage Maximizing the goal, a two-stage optimization strategy is adopted. When the number of deception sources When the number of deception sources is When the multi-stage neighborhood look-ahead mechanism is introduced, the neighborhood search is expanded with the deployed point as the center, the covered area is filtered by the sliding window mask, the potential value of the uncovered area is calculated by combining the distance attenuation weighted function, the candidate point priority is dynamically updated, the point with the highest potential value is selected to join the deployment point set, and the deception source coordinate vector is output. and coverage .

[0026] In one embodiment, calculating the potential value of the uncovered area in combination with a distance decay weighting function includes: Combined with the distance attenuation weighting function, the potential value of the uncovered area is calculated as

[0027] in, S n For uncovered areas, l For distance.

[0028] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0029] In one embodiment, Figure 3 As shown, a deception source optimization deployment device based on DG-MFCO is provided, including: a point set generation module 302, a mask matrix generation module 304 and a deception source optimization deployment module 306, wherein: The point set generation module 302 is configured to obtain a digital elevation model of the target area and obtain a ground candidate point set from the digital elevation model; perform vertical layered sampling of the airspace of the target area using a gridding method to generate a three-dimensional airspace point set containing elevation data; The mask matrix generation module 304 is used to construct a terrain occlusion-aware visual domain model based on the digital elevation model using a reference surface algorithm, determine the visibility of candidate ground points and 3D airspace points using ray tracing principles, and generate a 3D line of sight mask matrix. The deception source optimization deployment module 306 is used to generate a coverage vector for each ground candidate point based on the three-dimensional line of sight mask matrix; the coverage vector includes the coverage rate; with the goal of maximizing the coverage rate, a two-stage optimization strategy is used to dynamically update the priority of the ground candidate points, select the ground candidate points with the highest potential value to add to the deployment point set, and output the deception source coordinate vector.

[0030] The specific definitions of a DG-MFCO-based deception source optimization deployment device can be found in the above-mentioned definitions of a DG-MFCO-based deception source optimization deployment method and will not be further elaborated here. Each module in the aforementioned DG-MFCO-based deception source optimization deployment device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0031] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a deception source optimization deployment method based on DG-MFCO is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0032] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0033] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0034] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0035] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A deception source optimization deployment method based on DG-MFCO, characterized in that: The method comprises: Obtaining a digital elevation model of the target area and obtaining a set of ground candidate points from the digital elevation model; performing vertical layered sampling on the airspace of the target area according to a gridding method to generate a three-dimensional airspace point set containing elevation data; Based on the digital elevation model, a reference surface algorithm is used to construct a terrain occlusion-aware visual domain model. The visibility of candidate ground points and 3D airspace points is determined by ray tracing, and a 3D line of sight mask matrix is ​​generated. A coverage vector is generated for each ground candidate point based on the three-dimensional line of sight mask matrix; the coverage vector includes a coverage rate; with the goal of maximizing the coverage rate, a two-stage optimization strategy is adopted to dynamically update the priority of the ground candidate points, select the ground candidate points with the highest potential value to add to the deployment point set, and output the deception source coordinate vector.

2. The method according to claim 1, characterized in that Obtaining a ground candidate point set from the digital elevation model, including: A three-dimensional terrain grid containing plane coordinates and elevation values ​​is obtained from the digital elevation model, outlier correction and missing value filling are performed on the DEM data, the ground area is gridded at a fixed resolution, and candidate points that meet the deployment conditions are screened to form a set of ground candidate points as alternative locations for subsequent deception source deployment.

3. The method according to claim 1, characterized in that The airspace of the target area is vertically layered and sampled according to a gridding method to generate a three-dimensional airspace point set containing elevation data, including: The gridding method is used to perform vertical layered sampling of the airspace of the target area to generate a three-dimensional airspace point set covering 10m to 500m of the surface. , used to quantify the spatial coverage of the deceptive signal; each spatial point represents a stereo interference unit to be covered.

4. The method according to claim 1, wherein Based on the digital elevation model, a reference surface algorithm is used to construct a terrain occlusion-aware visual domain model. The visibility of candidate ground points and 3D airspace points is determined by ray tracing principles, and a 3D line of sight mask matrix is ​​generated, including: Based on the digital elevation model, a reference surface algorithm is used to construct a visual domain model with terrain occlusion awareness. The reference plane is constructed by calculating the deception source position and the auxiliary grid points near the target point. The mapped elevation of the target point on the reference plane is calculated. If the actual elevation of the target point is greater than or equal to the mapped elevation, it is judged to be visible; otherwise, it is invisible due to terrain occlusion.

5. The method according to claim 4, characterized in that The process of generating a 3D line of sight mask matrix includes: The size of the mask matrix is ​​determined according to the number of ground candidate points and 3D spatial points. The mask matrix is , represents the number of candidate ground points, Represents the number of three-dimensional spatial points. The number of rows of the mask matrix is ​​the number of candidate ground points, and the number of columns is the number of spatial points. For each combination of a ground candidate point and a 3D airspace point, the matrix elements are filled in according to the calculated visibility results. If a ground candidate point is judged to be visible to a 3D airspace point, the corresponding element in the mask matrix is ​​assigned a value of 1; if not, it is assigned a value of 0. In this way, all combinations of ground candidate points and 3D airspace points are traversed to complete the generation of the entire 3D line of sight mask matrix.

6. The method according to claim 1, characterized in that With the goal of maximizing coverage, a two-stage optimization strategy is used to dynamically update the priority of ground candidate points. The ground candidate points with the highest potential are selected to join the deployment point set, and the spoofing source coordinate vector is output, including: A two-stage optimization strategy is adopted. When the number of deception sources is not greater than a preset threshold, a parallel greedy algorithm is used to traverse all ground candidate points and select the points with the largest coverage to quickly form an initial coverage network. When the number of deception sources is greater than the preset threshold, a multi-stage neighborhood look-ahead mechanism is introduced to expand the neighborhood search centered on the deployed point. The covered area is filtered through a sliding window mask, and the potential value of the uncovered area is calculated in combination with a distance attenuation weighted function. The priority of the ground candidate points is dynamically updated, and the ground candidate point with the highest potential value is selected to join the deployed point set, and the deception source coordinate vector is output.

7. The method according to claim 6, characterized in that The potential value of uncovered areas is calculated using a distance decay weighting function, including: Combined with the distance attenuation weighting function, the potential value of the uncovered area is calculated as in, S n For uncovered areas, l For distance.

8. A deception source optimization deployment device based on DG-MFCO, characterized in that: The device comprises: A point set generation module is configured to obtain a digital elevation model of the target area and obtain a ground candidate point set from the digital elevation model; perform vertical layered sampling of the airspace of the target area according to a gridding method to generate a three-dimensional airspace point set containing elevation data; The mask matrix generation module is used to construct a terrain occlusion-aware visual domain model based on the digital elevation model using a reference surface algorithm. The visibility of candidate ground points and 3D airspace points is determined by ray tracing principles to generate a 3D line-of-sight mask matrix. A deception source optimization deployment module is configured to generate a coverage vector for each ground candidate point based on the three-dimensional line of sight mask matrix; the coverage vector includes a coverage rate; with the goal of maximizing the coverage rate, a two-stage optimization strategy is used to dynamically update the priority of the ground candidate points, select the ground candidate points with the highest potential value to add to the deployment point set, and output the deception source coordinate vector.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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