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题名:
Particle Filter for State and Parameter Estimation in Passive Ranging
作者: Wang Wan-ping Liao Sheng Xing Ting-wen
出版日期: 2009
会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems
会议日期: 2009
通讯作者: Wang Wan-ping Liao Sheng Xing Ting-wen
中文摘要: 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.
英文摘要: 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.
收录类别: Ei
语种: 英语
卷号: 257-261
文章类型: 会议论文
内容类型: 会议论文
URI标识: http://ir.ioe.ac.cn/handle/181551/7443
Appears in Collections:应用光学研究室(二室)_会议论文

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作者单位: 中国科学院光电技术研究所

Recommended Citation:
Wang Wan-ping Liao Sheng Xing Ting-wen. Particle Filter for State and Parameter Estimation in Passive Ranging[C]. 见:2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. 2009.
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文件名: 2009-073.pdf
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