基于遗传算法的改进无迹粒子滤波相对导航算法  

An Improved Relative Navigation Algorithm of Unscented Particle Filter Based on Genetic Algorithm

作　　者：邱琪涵 丁晓 孟秀云[1] QIU Qihan;DING Xiao;MENG Xiuyun(School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China;Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)

机构地区：[1]北京理工大学宇航学院,北京100081 [2]上海机电工程研究所,上海201109

出　　处：《火力与指挥控制》2025年第3期78-84,共7页Fire Control & Command Control

摘　　要：针对无人机编队飞行过程中相对导航系统存在测量误差问题,提出一种基于遗传算法的改进无迹粒子滤波相对导航系统信息融合算法。建立了无人机相对运动模型与组合导航系统测量模型。针对粒子滤波算法重要性密度函数选取的问题,将无迹卡尔曼滤波引入粒子滤波重要性采样环节,并在粒子滤波算法重采样阶段提出一种基于遗传算法的改进重采样方法。进行了数学仿真,仿真结果表明,该方法能够有效估计无人机相对运动信息,优于无迹粒子滤波算法和粒子滤波算法。In order to solve the problem of measurement error in the relative navigation system of UAV formation flight,an improved unscented particle filter(UPF)information fusion algorithm for relative navigation system is proposed.The relative motion model of UAV and the measurement model of integrated navigation system are established in this paper.To solve the problem of importance density function selection in particle filter algorithm,unscented Kalman filter(UKF)is introduced into importance sampling.In the resampling stage of particle filter algorithm,an improved method based on genetic algorithm is proposed to improve particle diversity Finally,a mathematical simulation is carried out,and the results show that the proposed method can effectively estimate the relative motion information of UAVS,which is better than the unscented particle filter algorithm and particle filter algorithm.

关 键 词：相对导航 粒子滤波 无迹卡尔曼滤波 遗传算法 

分 类 号：V249.3[航空宇航科学与技术—飞行器设计]