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针对测量噪声呈厚尾分布的情况以及粒子滤波器存在的粒子贫乏问题,提出了一种基于无迹卡尔曼滤波和云模型改进的遗传粒子滤波方法。首先用无迹卡尔曼滤波获得重要性函数,然后在重采样过程中利用云遗传算法对粒子进行优化,在进化过程中,通过Y云发生器进行变异操作,在现有粒子的附近搜索更优良的粒子。仿真结果表明,改进后的方法跟踪精度优于常规粒子滤波、规则粒子滤波、无迹粒子滤波和传统遗传粒子滤波。可有效地解决了粒子的贫乏问题,提高了相对导航估计精度。
Aiming at the situation that the measurement noise is thick tail and the problem of particle filter existing in particle filter, a genetic particle filter based on unscented Kalman filter and cloud model is proposed. Firstly, the importance function is obtained by unscented Kalman filter, then the particle is optimized by cloud genetic algorithm in the resampling process. In the evolution process, the Y cloud generator is used to perform the mutation operation and the search is better in the vicinity of the existing particles particle of. The simulation results show that the improved tracking accuracy is better than that of conventional particle filter, regular particle filter, unscented particle filter and traditional genetic particle filter. Which can effectively solve the problem of poor particles and improve the accuracy of relative navigation estimation.