计算机工程2012，Vol.38Issue(5)：176-178,182,4.
一种用于目标跟踪的改进粒子滤波算法
Improved Particle Filtering Algorithm for Target Tracking
摘要
Abstract
As the problems of estimation accuracy and particles degradation exist in the Particle Filtering(PF) algorithm, an improved PF algorithm is proposed. This algorithm which is based on PF uses the Unscented Kalman Filtering(UKF) to generate the proposal distribution so as to improve the filtering effect. It synchronizes the standard Markov Chain Monte Carlo(MCMC) sampling method and the unscented PF, which can reduce the effect that the traditional PF does not consider the current measurement, and makes the particles more diversification. Simulation results demonstrate that the algorithm has more significant advantages in tracking accuracy and filtering effect than other traditional PF algorithms.关键词
粒子滤波/目标跟踪/非线性滤波/扩展卡尔曼滤波/无迹卡尔曼滤波/马尔可夫链-蒙特卡洛Key words
Particle Filtering(PF)/target tracking/nonlinear filtering/Extended Kalman Filtering(EKF)/Unscented Kalman filtering(UKF)/ Markov chain Monte Carlo(MCMC)分类
信息技术与安全科学引用本文复制引用
张建安,赵修斌,李思佳..一种用于目标跟踪的改进粒子滤波算法[J].计算机工程,2012,38(5):176-178,182,4.基金项目
国家自然科学基金资助项目(61071014) （61071014）
空军工程大学电讯工程学院科研创新基金资助项目(DYCX1002) （DYCX1002）
