中文摘要：

针对粒子滤波（PF）算法由于粒子的数量和质量的影响、重要性密度函数不能直接求得、重采样过程中噪声无法优化而使粒子退化严重以致跟踪精度不高的问题，提出了遗传方差自适应（GVA）PF（GVAPF）算法。首先利用遗传算法从大量粒子中挑选初始粒子，改善初始粒子的质量。然后对重采样过程的噪声采用方差自适应进行实时修改，使得重要性密度函数更加逼近状态的真实分布。仿真结果表明：改进的算法明显优于标准PF算法。

英文摘要：

Aiming at problem of low tracking precision of particle filtering （PF） algorithm caused by quantity and quality effect of particle, and the importance density function can not be obtained directly and noise can not be optimized in resampling process,which give rise to particle degeneration, propose a genetic-variance adaptive PF （GVAPF） algorithm. Firstly, use genetic algorithm to select initial particles from large numbers of particles in order to improve quality of initial particles. And then, modify noise in resampling process by variance adaptive （YA） technology in real-time, and make the importance density function gets closer to true distribution of the state. Simulation results show that the improved algorithm is superior to the standard PF algorithm.