CDF-KF Algorithm for Conditionally Linear Gaussian State Space Models

摘要：

We propose a new algorithm, called the central difference filter -Kalman filter (CDF-KF) for conditionally linear Gaussian state space models. The linear state equation is firstly inserted into the measurement equation, and the CDF is applied to the new measurement and the nonlinear state equations to estimate the nonlinear states, where after the estimated means of the nonlinear states are substituted into the linear state equation and the original measurement equation to estimate the linear states using the Kalman filter (KF). Moreover, in order to improve the accuracy of the estimation, the estimated covariances of the nonlinear states are fed back to modify the estimations of the linear states. The simulation results of the proposed CDF-KF applying to target tracking show that it only consumes about 5％ the computing time required by the Rao-Blackwellized particle filter (RBPF), while the consistent filtering performance is kept.

关键词： signal processing Kalman filtering nonlinear estimation tracking

作者: Jian Jun Yin Jian Qiu Zhang Jin Zhao

作者单位: Electronic Engineering Department Fudan 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: 495-498

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）