Target tracking algorithm based on improved extend Kalman particle filter
基于改进扩展卡尔曼粒子滤波的目标跟踪算法*

Considering the problem of poor tracking accuracy and particle degradation in the traditional particle filter algorithm, a new improved particle filter algorithm with the Markov chain Monte Carlo (MCMC) and extended particle filter is discussed. The algorithm used Extend Kalman filter to generate a proposal distribution, which can integrate latest observation information to get the posterior probability distribution that was more in line with the true state. Meanwhile, the algorithm was optimized by MCMC sampling method, which maked the particles more diverse. The simulation results show that the improved extend Kalman particle filter solves particle degradation effectively and improves tracking accuracy.