基于改进蚁群算法的隐身无人机快速突防航路规划技术  

Rapid Penetration Path Planning of Stealth UAVs Based on Improved Ant Colony Optimization

作　　者：吴剑[1] 江泽军 朱效洲 张哲 WU Jian;JIANG Zejun;ZHU Xiaozhou;ZHANG Zhe(College of Information Engineering,Nanchang Hangkong University,Nanchang 330000,China;Defense Innovation Institute,Chinese Academy of Military Science,Beijing 100000,China;College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211000,China)

机构地区：[1]南昌航空大学信息工程学院,南昌330000 [2]军事科学院国防科技创新研究院,北京100001 [3]南京航空航天大学自动化学院,南京211000

出　　处：《电光与控制》2023年第12期18-23,共6页Electronics Optics & Control

基　　金：国家自然科学基金(61663032)。

摘　　要：针对复杂战场环境下的隐身飞行器的突防航路规划问题,提出了一种改进蚁群算法。引入相邻节点间的影响权重和航路选择权重,采用改进启发函数以增强目标点区域的导向性,提高搜索效率,保持解的多样性;设计了信息素动态调节的方式,提出新的信息素浓度更新策略,增强全局搜索能力。仿真结果表明,相比于传统蚁群算法和粒子群算法,改进蚁群算法能够有效地规避组网雷达威胁,从而提高隐身无人机的生存能力。此外,所提算法在计算效率和安全性上具有更好的性能,验证了该算法的有效性和优越性。To solve the problem of stealth UAV penetration path planning in complex battlefield environment,an Improved Ant Colony Optimization(IACO)is proposed.The weight of interaction between adjacent nodes and the path selection weight are introduced,and the improved heuristic function is adopted to enhance the guidance of the target point area,so as to improve the search efficiency and maintain the diversity of solutions.The mode of pheromone dynamic adjustment is designed,and a new pheromone concentration updating strategy is proposed to enhance the global search ability.The simulation results show that,compared with the traditional Ant Colony Optimization(ACO)and the Particle Swarm Optimization(PSO),the IACO can effectively avoid the threat of networked radar and thus improve the survivability of stealth UAVs.In addition,the proposed method has better performance in computing efficiency and safety,which verifies the effectiveness and superiority of the IACO.

关 键 词：航路规划 蚁群优化算法 隐身无人机 雷达散射截面 

分 类 号：V279[航空宇航科学与技术—飞行器设计]