摘要
针对现代战争中隐身无人机(UAV)在高严密的组网雷达防御体系下的生存及突防问题,提出了基于改进A-Star算法的隐身无人机战区突防航路规划技术。首先对隐身无人机突防过程进行了分析建模,分别建立了隐身无人机的运动学模型、动态雷达散射截面特性和组网雷达探测概率的计算模型。然后针对传统算法在解决隐身突防问题时的不足,充分考虑所规划航路时的快速性和安全性要求,设计了改进A-Star算法。在算法中引入了多层变步长搜索策略和无人机的姿态角信息,结合秩K融合准则,通过每段航迹上隐身无人机被组网雷达系统的发现概率来判断新航迹点的可行性。仿真结果表明,改进A-Star算法能够在复杂的组网雷达系统下快速生成更优的战区突防航路,具有一定的应用价值。
Aiming to solve the survival and penetration problems of the stealth Unmanned Aerial Vehicle(UAV)under highdefinition netted radar defense systems in modern warfare,this paper proposes a stealth UAV theater penetration path planning technology based on an improved A-Star algorithm.Firstly,the penetration process of the stealth UAV is analyzed and modeled,and the kinematics model of the stealth UAV,the dynamic radar cross section characteristics and the calculation model of the netted radar detection probability are established.Then,in view of the shortcomings of traditional algorithms in solving the problem of stealth penetration and taking into full consideration of the requirements of rapidity and safety in the planned route,an improved A-Star algorithm is designed.The multi-layer variable step size search strategy and the attitude angle information of UAVs are introduced into the algorithm.Further combined with the rank Kfusion criterion,this algorithm can judge the feasibility of the new track point by the detection probability of the netted radar system of the stealth UAV on each track.The simulation results show that the improved A-Star algorithm can quickly generate better penetration routes in the combat area under complex netted radar systems,exhibiting certain application value.
作者
张哲
吴剑
代冀阳
应进
何诚
ZHANG Zhe;WU Jian;DAI Jiyang;YING Jin;HE Cheng(School of Information Engineering,Nanchang Hangkong University,Nanchang 330063,China;School of Reliability and System Engineering,Beihang University,Beijing 100083,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2020年第7期248-258,共11页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金(61663032)
航空科学基金(2016ZC56003)
南昌航空大学研究生创新专项基金(YC2019026)。
作者简介
通信作者:吴剑,E-mail:wujiannchu@126.com。
