针对粒子滤波算法中的权值退化问题，提出了一种遗传算法辅助下的粒子滤波算法。利用遗传算法在选择继承性上的优势，将遗传算法中的选择、交叉和变异操作引入到粒子滤波算法中。在此基础上，将改进的粒子滤波算法与建立的全球定位系统(GPS)非线性动态状态空间模型结合应用于GPS 定位数据处理问题，通过采集实测GPS 数据将改进粒子滤波算法与基本粒子滤波算法做了比较。结果表明，遗传算法辅助下的粒子滤波可以增加有效粒子数目，有效解决粒子退化问题，可提高GPS 定位数据处理精确度。

Aiming at the weight degeneracy phenomena in Particle Filter(PF) algorithm, a resampling method improving the diversity based on Genetic Algorithm(GA)-aided PF is presented. It takes the advantages of GA in selection, crossover and inheritance, to make up the shortcomings of resampling. The evolutionary idea of GA is combined with PF by using selection and mutation to improve the weight degeneracy and the diversity of PF. This GA-aided PF is applied to the Global Positioning System(GPS) nonlinear dynamic state space model for data processing. The experimental results based on the collected real GPS data indicate that the GA-aided PF can increase the number of particle, and effectively solve the particle degradation problem; the estimation accuracy of GA-aided PF is better than that of PF.