Title: Particle smoothing via Markov chain Monte Carlo in general state space models

Authors: Meng Gao; Hui Zhang

Addresses: Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China ' School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an, 710072, China

Abstract: 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.

Keywords: Sequential Monte Carlo; SMC; particle filter; forward filtering-backward smoothing; Metropolis-Hastings.

DOI: 10.1504/IJCSM.2018.091733

International Journal of Computing Science and Mathematics, 2018 Vol.9 No.2, pp.181 - 188

Accepted: 16 Jun 2017
Published online: 14 May 2018 *

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