基于改进快速扩展随机树方法的隐身无人机突防航迹规划

Stealth penetration path planning for stealth unmanned aerial vehicle based on improved rapidly-exploring-random-tree

针对隐身无人机在日趋严密的雷达防御系统下的生存问题,提出了基于改进快速扩展随机树的隐身突防航迹规划方法.本文首先对隐身突防航迹规划中无人机的动态雷达散射截面积和雷达的发现准则这两个关键问题进行了分析和建模,然后针对现有算法在解决隐身飞机航迹规划问题时的不足,设计了改进快速扩展随机树算法,将无人机的雷达散射截面积随姿态变化的情况考虑到新节点生成中,并且结合滚动时域策略计算时域范围内所有节点的瞬时发现概率均值,以判断新节点可行性.仿真结果和对比研究表明,算法的改进策略能够处理隐身突防航迹规划的两个特性,并且可在复杂环境下快速生成更优的突防路径.

Because the air-defense radar net is increasingly dense in the modern warfare,a stealth penetration path planning scheme based on improved rapidly-exploring-random-tree （RRT） is proposed to address the flight survivability problem of the stealth unmanned aerial vehicles （UAV）.Firstly,two crucial characters of the stealth penetration path planning,the dynamic radar cross section （RCS） of the aircraft and the radar detection criterion,are analyzed and modeled.Secondly,an improved RRT is proposed to solve the path planning problem of the stealth UAV which has not been well handled by existing methods.When the improved algorithm generates a new vertex,the RCS variation according to different attitude angles is taken into consideration.Lastly,the feasibility of the new vertex is estimated through the average value of the instantaneous detection probability of several vertexes around it.This value is calculated with the receding horizon control strategy.The simulation result and the comparison study show that the two characters of the penetration path planning can be well handled by the proposed algorithm,and a better penetration route can be generated efficiently in complex scenarios.