Multi-sensor Information Fusion Extended Kalman Particle Filter

摘要：

In this paper, a new extended Kalman particle filter based information fusion is proposed for state estimation problem of nonlinear and non-Gaussian systems. It uses extended Kalman filter algorithm to update particles in particle filter, with which the local state estimated values can be calculated. The multi-sensor information fusion filter is obtained by applying the standard linear minimum variance fusion rule weighted by scales. The simulation results show that the proposed algorithm improves the accuracy of filter compared with single sensor.

关键词： information fusion extended Salman particle filter state estimation

作者: Mao Lin Liu Sheng

作者单位: Department of Automation Harbin Engineering University Harbin China department of Electronic Enginee Department of Automation Harbin Engineering University Harbin 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: 417-419

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）