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Particle Filter for State and Parameter Estimation in Passive Ranging
Wang Wan-ping Liao Sheng Xing Ting-wen
Volume257-261
2009
Language英语
Indexed ByEi
Subtype会议论文
AbstractOn-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.; 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.
Conference Name2009 IEEE International Conference on Intelligent Computing and Intelligent Systems
Conference Date2009
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7443
Collection应用光学研究室(二室)
Corresponding AuthorWang Wan-ping Liao Sheng Xing Ting-wen
Affiliation中国科学院光电技术研究所
Recommended Citation
GB/T 7714
Wang Wan-ping Liao Sheng Xing Ting-wen. Particle Filter for State and Parameter Estimation in Passive Ranging[C],2009.
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