A Method of Genetic Algorithm Optimized Extended Kalman Particle Filter for Nonlinear System State Estimation

A new method of genetic algorithm (GA) optimized the extended Kalman particle filter (EKPF) is proposed in this paper. The algorithm of extended Kalman particle filter is a suboptimal filtering algorithm with good performance for target tracking and non-linear tracking problem. In the implementation of the extended Kalman particle filter, a re-sampling scheme is used to decrease the degeneracy phenomenon and improve estimation performance. However, the target tracking mutation system status has poorer filtering precision. In order to overcome the problem of the extended Kalman particle filter, a novel filtering method called the genetic particle filter (GA-EKPF) is proposed in this paper. The genetic mechanism provides an important guiding ideology to solve the deprivation of particles. The proposed algorithm overcomes the deprivation of particles and enhances the filtering precision. Experimental results show that the performance of modified extended Kalman particle filter superiors to the standard particle filter (PF) and some other modified PFs.