An improved particle filtering algorithm based on observation inversion optimal sampling

被引：9
|
作者：
Hu Zhen-tao [1 ]
Pan Quan [1 ]
Yang Feng [1 ]
Cheng Yong-mei [1 ]
机构：
[1] Northwestern Polytech Univ, Coll Automat, Xian 710072, Peoples R China
来源：
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY | 2009年 / 16卷 / 05期
基金：
中国国家自然科学基金;
关键词：
particle filter; proposal distribution; re-sampling; observation inversion;
D O I：
10.1007/s11771-009-0135-y
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter, an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly, virtual observations were generated from the latest observation, and two sampling strategies were presented. Then, the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally, the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method, sampling particles can make full use of the latest observation information and the priori modeling information, so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter, the extended Kalman particle filter and the unscented particle filter.
引用
收藏
页码：815 / 820
页数：6
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