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作 者:傅嘉晨 付润定 张亚[1] Fu Jiachen;Fu Runding;Zhang Ya(School of Automation,Southeast University,Nanjing 210096,China;Nanjing Shenke Intelligent Technology Co.,Ltd.,Nanjing 210007,China)
机构地区:[1]东南大学自动化学院,南京210096 [2]南京深克智能科技有限责任公司,南京210007
出 处:《东南大学学报(自然科学版)》2023年第4期709-717,共9页Journal of Southeast University:Natural Science Edition
基 金:国家重点研发计划资助项目(2021ZD0112700);“十四五”装备预研专用技术资助项目(315057205).
摘 要:基于兵棋推演仿真软件构建用以测试导弹制导与反探测能力的设定场景,并参考人工势场法在路径规划领域的应用,设计导弹反探测航迹规划方法以躲避探测侦察.为了高效搜索人工势场法的最优参数,基于Kubernetes在多服务器上搭建分布式框架,实现分布式遗传算法,在并行数目为100的实验中,理论加速比在98.41~99.46之间,实验加速比平均为96.95.将基于分布式遗传算法和人工势场法的导弹反探测航迹规划方法应用于各种不同探测策略下的导弹制导场景.实验结果显示,改进的人工势场法比传统的人工势场法的平均得分提高11.9%,遗传算法的进化次数平均减少2.73轮.该分布式框架可用于任意容器化的并行仿真任务中.An imaginary scenario is constructed based on military chess simulation software to test the guidance and anti-detection capabilities of missiles.An anti-detection path planning method is proposed to avoid detection and reconnaissance based on the application of artificial potential field method.In order to efficiently search for the optimal parameters of the artificial potential field method,a distributed framework based on Kubernetes is developed on multiple servers to implement a distributed genetic algorithm.In experiments with a parallel number of 100,the theoretical acceleration ratio ranged from 98.41 to 99.46,and the experimental acceleration ratio was 96.95 on average.The missile anti-detection trajectory planning method based on the distributed genetic algorithm and artificial potential field method is applied to missile guidance scenarios under various detection strategies.The experimental results show that the improved artificial potential field method improves the average score by 11.9%over the traditional artificial potential field method,and the number of evolutionary rounds of the genetic algorithm is reduced by 2.73 on average.The distributed framework can be used in any containerized parallel simulation task.
关 键 词:分布式框架 遗传算法 人工势场法 反探测 航迹规划
分 类 号:TP275[自动化与计算机技术—检测技术与自动化装置]
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