Particle Filter for State and Parameter Estimation in Passive Ranging

摘要：

On-line state and parameter estimation is important and difficult in passive ranging. This paper proposes particle filter based on sequential Monte Carlo method for state estimation. And a kernel smoothing approach is introduced for the estimation of static model parameters. To demonstrate effectiveness of the proposed algorithms, the static parameters are calculated by kernel smoothing and states are estimated by Auxiliary Particle Filter (APF) in simulation experiment. The proposed algorithm achieves combined state and parameter satisfactory results.

关键词： bearing-only tracking passive ranging State Estimation Parameter Estimation Particle Filter

作者: WANG Wan-ping LIAO Sheng XING Ting-wen

作者单位: Institute of Optics and Electronics,Chinese Academy of Sciences Chendu,P.R.China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2072-2076

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）