Gaussian Particle Filtering Based on Coarse and Fine-scale Sampling on Target Tracking Algorithm

摘要：

Sequence Monte Carlo estimation for dynamic target involves recursive algorithm and predictive distributions of unobserved time varying signal based on noisy observations. A new Gaussian particle filtering based on coarse and fine-Scale coupled chain sampling is presented in this paper, which approximates the posterior distributions by single Gaussians. By using a coarser scale, the target state chain can run faster and better explore the posterior while a fine scale sampling can guarantee the accuracy of target tracking; the coupled chain is included updates that allow target state information to pass between the two scales. Simulation result show the improved GPF (Gaussian Particle Filtering) reduces the complexity and ensures the accuracy of target tracking.

关键词： Gaussian particle filtering (GPF) Coarse and fine-scale coupled sampling Target tracking posterior distribution Metropolis sampling

作者: Zhai Yongzhi Jing Zhanrong

作者单位: Colege of Electronics Information,Northwestern Polytechnical University,Xian 710072,China

会议类型: 国际会议

会议名称: The 2nd International Symposium on Systems and Control in Aeronautics and Astronautics(第二届航空航天系统与控制国际会议 ISSCAA 2008)

会议地点: 深圳

会议语种:英文

页码: 177-180

在线出版日期: 2008-12-10（万方平台首次上网日期，不代表论文的发表时间）