| 摘要: |
| 针对联合侦察筹划中任务规划阶段机动侦察平台阵位与路线确定困难的问题，提出了一种基于粒子群优化-稀疏A星(Particle Swarm Optimization—Sparse A-star,PSO-SAS)算法的规划方法。该方法综合考虑侦察装备机动性能以及敌火力威胁、地形等因素，在侦察阵位规划上，建立了阵位综合评估模型，并利用粒子群算法进行阵位寻优；在路线规划上，采用稀疏A*算法进行航迹规划，通过将机动性能、安全距离、路程等约束引入搜索过程，缩短最优路线的计算时间。仿真试验验证了所提方法生成的侦察阵位和路线能够满足侦察任务要求。 |
| 关键词: 联合作战 侦察筹划 任务规划 粒子群优化 稀疏A星算法 |
| DOI： |
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| 基金项目: |
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| Joint Reconnaissance Mission Planning Based on PSO-SAS Algorithm |
| LIU Ran,WANG Bo,LI Pengwei |
| (1.Beijing Institute of Information Technology,Beijing 100094,China;2.The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210001,China) |
| Abstract: |
| In view of the difficulty in determining the position and route of mobile reconnaissance platform in the mission planning stage of joint reconnaissance planning,a method of reconnaissance planning based on particle swarm optimization—sparse A-star(PSO-SAS) algorithm is proposed,which considers the flexibility of the reconnaissance equipment,the menace of hostile fire,terrain and so on.For reconnaissance position planning,a comprehensive evaluation model is established,and particle swarm optimization algorithm is used to find the optimal solution.The spares A-star algorithm is used for route planning,and the constraints such as performance,safe distance and distance are introduced into the search process,so that the optimal route can be generated in a shorter time.Simulation experiments verify that the reconnaissance positions and routes generated by the method can meet the requirements of reconnaissance mission. |
| Key words: joint operation reconnaissance planning task planning particle swarm optimization sparse A-star algorithm |
