计算机应用研究2011，Vol.28Issue(5)：1634-1636,1643,4.DOI:10.3969/j.issn.1001-3695.2011.05.010
基于改进扩展卡尔曼粒子滤波的目标跟踪算法
Target tracking algorithm based on improved extend Kalman particle filter
摘要
Abstract
Considering the problem of poor tracking accuracy and particle degradation in the traditional particle filter algorithm, discussed a new improved particle filter algorithm with the Markov chain Monte Carlo (MCMC) and extended particle filter.The algorithm used extend Kalman filter to generate a proposal distribution, which could integrate latest observation information to get the posterior probability distribution that was more in line with the true state.Meanwhile, optimized the algorithm by MCMC sampling method, which made the particles more diverse.The simulation results show that the improved extend Kalman particle filter solves particle degradation effectively and improves tracking accuracy.关键词
目标跟踪/粒子滤波/扩展卡尔曼滤波/马尔可夫链蒙特卡罗方法/非线性系统Key words
target tracking/ particle filter(PF)/ extend Kalman filter/ Markov chain Monte Carlo (MCMC)/ nonlinear system分类
信息技术与安全科学引用本文复制引用
王华剑,景占荣,羊彦..基于改进扩展卡尔曼粒子滤波的目标跟踪算法[J].计算机应用研究,2011,28(5):1634-1636,1643,4.基金项目
国家自然科学基金资助项目(60501004) （60501004）
