陈 涛^{1, 2}，李由之^{1, 2}，黄湘松^{1, 2}
AuthorsHTML:陈 涛^{1, 2}，李由之^{1, 2}，黄湘松^{1, 2}
AuthorsListE:Chen Tao ^{1, 2}，Li Youzhi ^{1, 2}，Huang Xiangsong ^{1, 2}
AuthorsHTMLE:Chen Tao ^{1, 2}，Li Youzhi ^{1, 2}，Huang Xiangsong ^{1, 2}
Unit:1. 哈尔滨工程大学信息与通信工程学院，哈尔滨 150001；
2. 哈尔滨工程大学先进船舶通信与信息技术工业和信息化部重点实验室，哈尔滨 150001
Unit_EngLish:1. School of Information and Communication Engineering，Harbin Engineering University，Harbin 150001，China；
2. Key Laboratory of Advanced Marine Communication and Information Technology，Ministry of Industry and Information Technology，Harbin Engineering University，Harbin 150001，China
Abstract_Chinese:针对电子侦察系统中反辐射无人机群进行辐射源无源定位时机群的编队形式会对定位精度产生影响的问题，将克拉美-罗界(Cramer-Rao lower bound，CRLB)作为定位精度方面的优化目标，与其他优化目标、约束一起引入机群的航迹规划中，使无人机群运动过程中保持良好编队，确保无源定位精度．文中针对多优化目标复杂环境中航迹规划算法寻优能力不高的问题，提出了一种基于改进多目标蝗虫算法(IMOGOA)的无人机群3维航迹规划方法，通过对MOGOA的选择方式、收敛参数进行改进从而提高算法的收敛性能以及全局搜索性能．首先，建立无人机群航迹规划的运动学模型，并引入距离约束，除定位精度以外还引入了路程、威胁代价等作为航迹规划的优化目标函数，然后，对改进多目标蝗虫算法进行详细说明，最后设计基于IMOGOA的无人机群航迹规划方案的算法流程，并在设定场景中对该算法的性能进行了仿真分析．结果表明，所提出的IMOGOA能够成功地规划出无人机群从初始位置到辐射源位置处的3维航迹，同时使无人机群在运动过程中保持良好的定位精度，经IMOGOA规划的机群编队定位精度最高可达1.2%，性能明显优于正方形编队和随机编队，并通过将IMOGOA与原始蝗虫算法(GOA)、原始多目标蝗虫算法进行对比，结果表明IMOGOA的收敛速度比MOGOA快11.1%，搜索性能相较GOA提升13.8%．
Abstract_English:Unmanned aerial vehicles(UAVs)are used in electronic reconnaissance systems for anti-radiation．But the formation of UAVs influences the passive location accuracy．To address this problem，we introduced the Cramer-Rao lower bound(CRLB)into UAVs path planning to optimize the passive location accuracy along with other optimization objectives and constraints．Passive location accuracy is thereby ensured by maintaining good formation during the UAVs movement．Second，to address the poor optimization ability of the existing path planning algorithm，we propose a three-dimensional UAVs path planning method based on the improved multi-objective grasshopper optimization algorithm(IMOGOA)．We improved the performance of the MOGOA by optimizing its selection method and convergence parameters．First，we established the path planning model in which we introduced a distance constraint．In addition to the passive location accuracy，we introduced the distance and threat cost as optimization functions of path planning，followed by a detailed description of the IMOGOA．Finally，we designed the UAVs path planning process based on the IMOGOA along with the simulation and analysis of the algorithm’s performance．The results show that the proposed IMOGOA can successfully plan a three-dimensional trajectory from the initial position to the source position that causes the UAV to maintain good passive location accuracy during movement．The passive location accuracy of the formation planned by IMOGOA reaches 1.2% with obviously better performance than square and random formations．Compared with the original grasshopper optimization algorithm (GOA) and MOGOA，the results show that the convergence speed of IMOGOA is 11.1% faster than that of MOGOA，and the search performance is 13.8% higher than that of GOA．
Keyword_Chinese:反辐射无人机；航迹规划；多目标蝗虫算法；定位精度
Keywords_English:anti-radiation unmanned aerial vehicle；path planning；multi-objective grasshopper optimization algorithm(MOGOA)；location accuracy

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