多元威胁环境下无人机集群隐身航迹规划算法

doi:10.12305/j.issn.1001-506X.2025.09.18

摘要/Abstract

摘要：

针对现代战争中无人机（unmanned aerial vehicle，UAV）集群在多元威胁环境下的航迹规划及平台安全问题，提出UAV集群隐身航迹规划算法。首先，建立多元威胁环境模型。然后，结合UAV雷达散射截面设计考虑UAV航程、组网雷达探测概率、高射炮威胁概率的集群隐身航迹规划综合代价函数。在此基础上，以最小化UAV集群综合代价函数为优化目标，以满足航迹可行性判定及UAV集群动力学限制为约束条件，构建多元威胁环境下UAV集群隐身航迹规划优化模型。最后，采用改进A*算法对上述优化模型进行求解。仿真结果表明，与现有算法相比所提算法能够在保证各UAV航迹可行性及其动力学性能约束的条件下，有效降低UAV集群综合代价。所提算法能够达到提升集群航迹隐身性能的目的。

关键词: 隐身航迹规划, 无人机集群, 多元威胁环境, A*算法, 综合代价函数

Abstract:

Aiming at the paths planning and platform security issue of unmanned aerial vehicle （UAV） swarm in modern warfare in the multiple-threat environment, a stealthy paths planning algorithm for UAV swarm is proposed. Firstly, a multiple-threat environment model is established. Then, a comprehensive cost function for swarm stealthy paths planning considering UAV’s range, networked radar detection probability, and antiaircraft guns threat probability is designed by combining with UAV radar cross section. On this basis, the optimization objective is to minimize the comprehensive cost function of the UAV swarm in order to meet the paths feasibility determination and UAV swarm dynamics constraints as constraints, to construct the optimization model of UAV swarm stealthy paths planning in a multiple-threat environment. Finally, the improved A* algorithm is used to solve the above optimization model. The simulation results show that in comparison to existing algorithms, the proposed algorithm can effectively reduce the comprehensive cost of the UAV swarm under the condition of ensuring the feasibility of each UAV path and its dynamics performance constraints. The proposed algorithm can achieve the purpose of improving the stealthy performance of the swarm’s paths.

Key words: stealthy paths planning, unmanned aerial vehicle（UAV） swarm, multiple-threat environment, A* algorithm, comprehensive cost function

中图分类号:

TN 957

闻雯, 时晨光, 周建江. 多元威胁环境下无人机集群隐身航迹规划算法[J]. 系统工程与电子技术, 2025, 47(9): 2971-2984.

Wen WEN, Chenguang SHI, Jianjiang ZHOU. Stealthy paths planning algorithm for UAV swarm in multiple-threat environment[J]. Systems Engineering and Electronics, 2025, 47(9): 2971-2984.

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无人机编号	起点/km	终点/km
无人机1	（5，18）	（70，85）
无人机2	（10，15）	（78，55）
无人机3	（5，25）	（90，77）
无人机4	（5，35）	（50，95）
无人机5	（20，10）	（89，40）
无人机6	（5，40）	（40，70）
无人机7	（25，5）	（95，70）
无人机8	（50，5）	（25，85）
无人机9	（70，15）	（5，60）
无人机10	（75，20）	（5，85）

雷达编号	位置
雷达1	（50，50）
雷达2	（25，50）
雷达3	（50，25）
雷达4	（25，25）

算法	航程/km	综合代价	运行时间/s
所提算法	2580	78.16	7.74
对比算法	2536	108.73	9.31

算法	航程/km	综合代价	运行时间/s
所提算法	4535	143.26	21.88
对比算法	4497	191.86	24.54

固定障碍物编号	顶点1	顶点2	顶点3	顶点4
1	（75，60）	（89，60）	（75，73）	（89，73）
2	（74，35）	（85，35）	（74，46）	（85，46）
3	（49，70）	（60，70）	（49，81）	（60，81）