Particle smoothing via Markov chain Monte Carlo in general state space models
Gao, Meng1; Zhang, Hui2
ISSN1752-5055
2018
卷号9期号:2页码:181-188
关键词Dynamical systemsMarkov processesNumerical methodsState estimationState space methods
DOI10.1504/IJCSM.2018.091733
产权排序(1) Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai; 264003, China; (2) School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an; 710072, China
作者部门海岸带信息集成与综合管理实验室
英文摘要Sequential Monte Carlo (SMC) methods (also known as particle filter) provide a way to solve the state estimation problem in nonlinear non-Gaussian state space models (SSM) through numerical approximation. Particle smoothing is one retrospective state estimation method based on particle filtering. In this paper, we propose a new particle smoother. The basic idea is easy and leads to a forward-backward procedure, where the Metropolis-Hastings algorithm is used to resample the filtering particles. The goodness of the new scheme is assessed using a nonlinear SSM. It is concluded that this new particle smoother is suitable for state estimation in complicated dynamical systems. Copyright ? 2018 Inderscience Enterprises Ltd.
文章类型article
收录类别EI
语种英语
研究领域[WOS]Control Systems
EI主题词Dynamical systems; Markov processes; Numerical methods; State estimation; State space methods
EI入藏号20182205239345
引用统计
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/25215
专题中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
中科院海岸带环境过程与生态修复重点实验室

作者单位1.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai; 264003, China;
2.School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an; 710072, China

推荐引用方式
GB/T 7714Gao, Meng,Zhang, Hui. Particle smoothing via Markov chain Monte Carlo in general state space models[J],2018,9(2):181-188.
APAGao, Meng,&Zhang, Hui.(2018).Particle smoothing via Markov chain Monte Carlo in general state space models.,9(2),181-188.
MLAGao, Meng,et al."Particle smoothing via Markov chain Monte Carlo in general state space models".9.2(2018):181-188.


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