The Polynomial Predictive Particle Filter

摘要：

We firstly constructed a new dynamic state space model with little exact knowledge of the original state dynamics by using the polynomial predictive filter and state dimension extension. Then a particle filter was used to estimate the extended state, where the sum of the extended particle weights was applied to test whether the filter is convergent or not. Finally the estimate of the original state was obtained by wiping off the components corresponding to the backward time steps. Simulation results demonstrate that, for unknown state dynamics, where the existed particle filter (PF) diverges, the proposed polynomial predictive particle filter (PPPF) still works well.

关键词： particle filtering polynomial predictive filter simulation tracking

作者: Jian Jun Yin Jian Qiu Zhang Yu Gao

作者单位: Electronic Engineering Department Fudan University Shanghai China School of Electrical Engineering Shanghai Dian Ji University Shanghai China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 527-531

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）