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.
International Journal of Computing Science and Mathematics, 2018 Vol.9 No.2, pp.181 - 188
Accepted: 16 Jun 2017
Published online: 14 May 2018 *