An unmanned aerial vehicle cooperative optimal coverage detection method for aerial cluster targets

Abstract

The application discloses a kind of unmanned aerial vehicle cooperative optimal coverage detection methods for aerial cluster target, comprising the following steps: establishing target probability distribution model under radar detection and unmanned aerial vehicle detection model;Based on target probability part model and unmanned aerial vehicle detection model, establish cluster cooperative coverage detection optimization objective function;According to the real-time detection result of radar and unmanned aerial vehicle, based on the detection result, the optimization objective function is iteratively optimized, and the coverage detection of target group is realized.The unmanned aerial vehicle cooperative optimal coverage detection method for aerial cluster target disclosed in the application can quickly generate the desired unmanned aerial vehicle formation and line-of-sight direction under multiple error inputs, and realize high coverage rate detection of target cluster.

Description

Technical Field

[0001] This invention relates to a method for cooperative optimal coverage detection of aerial swarm targets by unmanned aerial vehicles (UAVs), belonging to the field of aircraft control technology. Background Technology

[0002] In many cases, it is necessary to use drone swarms to coordinate regional coverage detection.

[0003] For the problem of collaborative detection by UAVs, most existing technologies are based on ideal assumptions and do not take into account engineering constraints such as information errors, control errors, and quantity limitations that exist in reality. Moreover, most algorithms are time-consuming and difficult to apply to highly dynamic, real-world swarm warfare.

[0004] For example, existing technologies disclose a region coverage detection algorithm that relies on a time window of the target area for clustering. Then, it incrementally generates a multi-UAV and cooperative trajectory planning problem using each cluster and prepared UAVs, and finally solves the trajectory planning problem using the simplex algorithm. However, this algorithm achieves coverage detection of static areas based on a defined initial UAV formation through task division and trajectory planning, and is not suitable for real-time coverage detection tasks in dynamic combat scenarios.

[0005] For example, existing technologies also include linear programming models based on ant colony algorithms and hexagonal grids to cover different areas. These models generate drone area coverage search trajectories based on actual drone performance and time optimization, which improves the coverage search efficiency of small drone swarms. However, these algorithms can only be applied to scenarios with fixed target areas and are not suitable for covering and detecting high-speed moving target swarms.

[0006] Therefore, it is necessary to conduct more in-depth research on the collaborative coverage detection method of UAV swarms in order to solve the above problems. Summary of the Invention

[0007] To overcome the above problems, the inventors conducted in-depth research and proposed a UAV cooperative optimal coverage detection method for aerial swarm targets, comprising the following steps:

[0008] S1. Establish a target probability distribution model under radar detection and a UAV detection model;

[0009] S2. Establish a cluster collaborative coverage detection optimization objective function based on the target probability partial model and the UAV detection model;

[0010] S3. Based on the real-time detection results from radar and UAVs, the objective function is iteratively optimized to achieve coverage detection of the target group.

[0011] Preferably, in S1, the target probability distribution model under radar detection is a two-dimensional planar model.

[0012] Preferably, in S1, the target probability distribution model under radar detection is expressed as:

[0013]

[0014]

[0015] Among them, P kk Let x represent any point within the m-th grid after the planar region has been discretized into a grid, with coordinates x. k ,y k ; This represents the probability of a target appearing at that point, where j is the target's index, n represents the total number of targets in the target cluster, and T... j Let j represent the j-th target. Indicates target T j Appears at point P kk (x k ,y k The probability of ) Indicates the target T under radar detection j Position, σ x ,σ y This indicates the error in the plane between the radar-detected target position and its actual position.

[0016] Preferably, σ x ,σ y Satisfying the criteria for a normal distribution, expressed as:

[0017] σ x =σ D cos(α E )*cos(α A )+D xy σ Az sin(α A )+Dσ EL sin(α E )*cos(α A )

[0018] σ y =σ D cos(α E )*sin(α A )+D xy σ AZ cos(α A )+Dσ EL sin(α E )*sin(α A )

[0019] Where, σ D σ represents the range detection error of the radar.AZ σ represents the azimuth detection error of the radar. EL α represents the radar's elevation angle detection error. A α represents the azimuth angle of the radar target detection. E This indicates the elevation angle of the target detected by the radar.

[0020] Preferably, the UAV detection model is represented as follows:

[0021]

[0022] in, U-shaped drone i Detection point P kk The probability, U-shaped drone i With point P kk The distance, R represents the distance of the drone U. i field of view radius, Point P kk With U drones i The angles formed, α1, α2, and α3 are settable field angle parameters, and k1, k2, and k3 are settable probability parameters.

[0023] Preferably, in S2, the optimization objective function is set as follows:

[0024]

[0025] Where Reward(U) is the optimization objective, i is the UAV number, and K... i Let ω1, ω2, ω3, and ω4 be the distance function between the i-th drone and the nearest drone. Reword i For U drones i The total detection gain is given by N, where N represents the total number of UAVs, M represents the total number of discrete grids, M = k × k, k is the number of single-sided grids, and m is the grid number.

[0026] Preferably, in S3, the Grey Wolf optimization algorithm is used to iteratively optimize the objective function.

[0027] The beneficial effects of this invention include:

[0028] (1) Under multiple error inputs, it can quickly generate the desired UAV formation and line of sight, and achieve high coverage detection of the target cluster;

[0029] (2) It meets the flight safety constraints of UAVs and can meet the needs of high dynamic coverage detection. Attached Figure Description

[0030] Figure 1 A flowchart illustrating a preferred embodiment of the UAV cooperative optimal coverage detection method for aerial swarm targets according to the present invention is shown.

[0031] Figure 2 The example shows the target group arranged in rectangular, triangular, and circular arrays in Example 1, with iterative optimization of the coverage detection results;

[0032] Figure 3 The example in Example 1 shows the relationship between the number of iterations and the computation time when the target group is arranged in a triangular array. Detailed Implementation

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present invention will become clearer and more apparent.

[0034] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments. Although various aspects of embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated otherwise.

[0035] According to the present invention, a method for cooperative optimal coverage detection of aerial swarm targets by unmanned aerial vehicles (UAVs) is provided, such as... Figure 1 As shown, it includes the following steps:

[0036] S1. Establish a target probability distribution model under radar detection and a UAV detection model;

[0037] S2. Establish a cluster collaborative coverage detection optimization objective function based on the target probability partial model and the UAV detection model;

[0038] S3. Based on the real-time detection results from radar and UAVs, the objective function is iteratively optimized to achieve coverage detection of the target group.

[0039] In S1, the target probability distribution model under radar detection is a two-dimensional planar model. Since the formation of UAV swarms generally does not have a large vertical drop, they can be detected by the UAV's field of vision in the vertical direction. Therefore, a two-dimensional planar model is sufficient to achieve good results in the target probability distribution model.

[0040] Preferably, in S1, the target probability distribution model under radar detection is expressed as:

[0041]

[0042] Among them, P kkThis represents any point within a grid after a planar region has been discretized into a grid, and its coordinates are x. k ,y k ; This represents the probability of a target appearing at that point, where j is the target's index, n represents the total number of targets in the target cluster, and T... j Let j represent the j-th target. Indicates target T j Appears at point P kk (x k ,y k The probability of ) Indicates the target T under radar detection j Position, σ x ,σ y This indicates the error in the plane between the radar-detected target position and its actual position.

[0043] Preferably, σ x ,σ y Satisfying the criteria for a normal distribution, expressed as:

[0044] σ x =σ D cos(α E )*cos(α A )+D xy σ AZ sin(α A )+Dσ EL sin(α E )*cos(α A )

[0045] σ y =σ D cos(α E )*sin(α A )+D xy σ AZ cos(α A )+Dσ EL sin(α E )*sin(α A )

[0046] Where, σ D σ represents the range detection error of the radar. AZ σ represents the azimuth detection error of the radar. EL α represents the radar's elevation angle detection error. A α represents the azimuth angle of the radar target detection. E This indicates the elevation angle of the target detected by the radar.

[0047] The UAV detection model is represented as follows:

[0048]

[0049] in, U-shaped drone i Detection point P kk The probability, U-shaped drone i With point P kk The distance, R represents the distance of the drone U. i field of view radius, Point P kk With U drones i The angles formed, α1, α2, and α3 are settable field angle parameters, and k1, k2, and k3 are settable probability parameters.

[0050] The inventors discovered that during actual detection missions, the detection effectiveness of the photoelectric pod is related to the angle between the target and the UAV's line of sight. Targets appearing in the central area of ​​the field of view are often easier to identify and track stably, while the effect is slightly worse in the peripheral areas. Furthermore, due to the pod's control pointing error, unexpected targets sometimes appear at the very edge of the field of view, and targets that should be in the field of view are sometimes lost. In this invention, a UAV detection model divides the field of view into three areas: a central area, a peripheral area, and an uncertain detection area. The distance and the angle between the target and the line of sight are used to determine whether the target is within the visible area. Different detectability probability values ​​are assigned to the target based on the size of the angle. Only when the target's line of sight angle appears in the field of view, i.e., in the central or peripheral areas, is it considered to be seen. Although there is also a gain in the potentially detectable area, it is not considered to have been detected and can be repeatedly detected.

[0051] In S2, the optimization objective function is set as follows:

[0052]

[0053] Where Reward(U) is the optimization objective, i is the UAV number, and K... i Let ω1, ω2, ω3, and ω4 be the spacing function for the i-th drone, used to ensure safe distances between different drones in a drone swarm. ω1, ω2, ω3, and ω4 are settable parameters. d represents the distance between the i-th drone and its nearest neighbor. i For U drones i The total detection gain is given by N, where N represents the total number of UAVs, M represents the total number of discrete grids, m = 1, 2, ..., M = k × k, k is the number of single-sided grids, and m is the grid number.

[0054] According to the present invention, the set of unmanned aerial vehicles is U{U1,U2,…U i …U NEach UAV has four parameters: radius R, field of view α, line of sight θ, and position coordinates (x,y).

[0055] According to the present invention, the optimization objective function takes maximizing detection gains as the optimization objective and the position and line-of-sight direction of the UAV as optimization decision variables. It fully considers the low detection accuracy constraint of radar for small groups of targets, the limited field of view constraint of UAV, and the detection characteristics of visual recognition algorithm. It generates the formation of our UAV and the desired line-of-sight direction in real time based on the error input of the radar, so as to achieve high coverage detection of target clusters.

[0056] Preferably, in S3, the Grey Wolf optimization algorithm is used to iteratively optimize the objective function. The Grey Wolf algorithm has the characteristics of simple structure, easy implementation and high solution efficiency.

[0057] The Grey Wolf Optimization Algorithm is a novel swarm intelligence algorithm proposed in 2015. It is an optimization algorithm based on the hunting behavior of wolf packs. For details, please refer to reference

[22] .

[0058] Rui W, Shiyao H, Wenqian Z, et al. An improved grey wolf optimizer for minimizing drilling deformation and residual stress in AA2024 sheet[J]. The International Journal of Advanced Manufacturing Technology, 2024, 130(9-10): 4443-4458. Further details are omitted in this invention.

[0059] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0060] Example

[0061] Example 1

[0062] The simulation experiment, which involves collaborative coverage detection by a swarm of drones, includes the following steps:

[0063] S1. Establish a target probability distribution model under radar detection and a UAV detection model;

[0064] S2. Establish a cluster collaborative coverage detection optimization objective function based on the target probability partial model and the UAV detection model;

[0065] S3. Based on the real-time detection results of radar and UAVs, the objective function is iteratively optimized to achieve coverage detection of the target group.

[0066] In S1, the target probability distribution model under radar detection is expressed as:

[0067]

[0068] σ x ,σ y Satisfying the criteria for a normal distribution, expressed as:

[0069] σ x =σ D cos(α E )*cos(α A )+D xy σ AZ sin(α A )+Dσ EL sin(α E )*cos(α A )

[0070] σ y =σ D cos(α E )*sin(α A )+D xy σ AZ cos(α A )+Dσ EL sin(α E )*sin(α A )

[0071] The UAV detection model is represented as follows:

[0072]

[0073] In S2, the optimization objective function is set as follows:

[0074]

[0075] In S3, the gray wolf optimization algorithm is used to iteratively optimize the objective function. The gray wolf optimization algorithm sets the number of wolves to 30 and the number of algorithm iterations to 10.

[0076] During the simulation, the number of drones in the drone swarm was set to 5, the drone field of view angle was 7°, the drone field of view range was 1000m, and the adjustable range of the field of view was -20° to 20°.

[0077] The radar and UAV error parameters are set as shown in Table 1:

[0078] Table 1

[0079]

[0080]

[0081] The parameters involved in the simulation are shown in Table 2:

[0082] Table 2

[0083]

[0084] The target group was arranged in rectangular, triangular, and circular arrays, and the coverage detection results were iteratively optimized as follows: Figure 2 As shown in the figure, for different target array configurations, an average coverage rate of over 98% can be achieved after 8 iterations, with the standard deviation of the coverage rate below 8%, essentially guaranteeing a coverage rate of over 90%. If 10 iterations are performed, the average coverage rate reaches over 99.4%, and the standard deviation also decreases to below 3.5%, stably achieving a coverage rate of over 95%.

[0085] The onboard computer was used for iterative optimization, with the target group arranged in a triangular array. The relationship between the number of iterations and the computation time was recorded, and the results are as follows: Figure 3 As shown in the figure, it can be seen that with high probability coverage of the target cluster, 10 optimization iterations can be completed within 1.2 seconds to achieve high coverage, which means dynamic real-time detection can be achieved.

[0086] The present invention has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present invention based on these embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A method for optimal coverage detection of aerial swarm targets by UAVs, characterized in that, The method comprises the following steps: S1, establishing a target probability distribution model under low detection precision constraint based on radar detection error, and establishing a UAV detection model under UAV detection field of view constraint based on the distance and angle of the target point to the UAV; S2, establishing a cluster cooperative coverage detection optimization objective function based on the target probability distribution model and the UAV detection model, with the goal of maximizing detection benefit; S3, based on the real-time detection results of the radar and the UAV, iteratively optimizing the optimization objective function based on the detection results, and obtaining the UAV position and line of sight direction through iteration to realize coverage detection of the target cluster.

2. The UAV cooperative optimal coverage detection method for aerial cluster targets according to claim 1, characterized in that: In S1, the target probability distribution model under radar detection is a planar two-dimensional model.

3. The UAV cooperative optimal coverage detection method for aerial cluster targets according to claim 1, characterized in that: In S1, the target probability distribution model under radar detection is expressed as: where P kk represents any point in the grid after the planar region is discretized into a grid, and its coordinates are x k ,y k ; represents the probability of the target appearing at the point, j is the serial number of the target, and n represents the total number of targets in the target cluster, T j represents the jth target, represents the probability of the target T j appearing at the point P kk (x k ,y k ), represents the position of the target T j detected by the radar, and σ x ,σ y represents the error of the position of the target detected by the radar and the actual position in the plane.

4. The UAV cooperative optimal coverage detection method for aerial cluster targets according to claim 3, characterized in that: σ x ,σ y satisfy the normal distribution standard, expressed as: σ x = σ D cos(α E )*cos(α A )+D xy σ AZ sin(α A )+Dσ EL sin(α E )*cos(α A ) σ y = σ D cos(α E )*sin(α A )+D xy σ AZ cos(α A )+Dσ EL sin(α E )*sin(α A ) where σ D represents the range detection error of the radar, σ AZ represents the azimuth angle detection error of the radar, σ EL represents the elevation angle detection error of the radar, α A represents the azimuth angle of the radar detecting the target, α E represents the elevation angle of the radar detecting the target.

5. The UAV cooperative optimal coverage detection method for aerial cluster targets according to claim 1, characterized in that: The UAV detection model is expressed as: wherein, represents the unmanned aerial vehicle U i the detection point P kk , the probability, represents the distance between the unmanned aerial vehicle U i and the point P kk , R represents the radius of the field of view of the unmanned aerial vehicle U i , represents the angle between the point P kk and the unmanned aerial vehicle U i , a1, a2, a3 are settable field of view angle parameters, and k1, k2, k3 are settable probability parameters.

6. The UAV cooperative optimal coverage detection method for aerial cluster targets according to claim 1, characterized in that: In S2, the optimization objective function is set as: wherein, Reward(U) is an optimization target, i is the number of the UAV, K i is the ith UAV spacing function, ω1, ω2, ω3, ω4 are settable parameters, d represents the distance between the ith UAV and the nearest UAV, Reward i is the total detection reward of the UAV U i , N represents the total number of UAVs, M represents the total number of discrete grids, and m is the number of the grid.

7. The UAV cooperative optimal coverage detection method for aerial cluster targets according to claim 1, characterized in that: In S3, the grey wolf optimization algorithm is used to iteratively optimize the optimization objective function.

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