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Invertible Particle-Flow-Based Sequential MCMC With Extension to Gaussian Mixture Noise Models

Li, Yunpeng, Pal, Soumyasundar and Coates, Mark J. (2019) Invertible Particle-Flow-Based Sequential MCMC With Extension to Gaussian Mixture Noise Models IEEE TRANSACTIONS ON SIGNAL PROCESSING, 67 (9). pp. 2499-2512.

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Abstract

Sequential state estimation in non-linear and non-Gaussian state spaces has a wide range of applications in statistics and signal processing. One of the most effective non-linear filtering approaches, particle filtering, suffers from weight degeneracy in high-dimensional filtering scenarios. Several avenues have been pursued to address high-dimensionality. Among these, particle flow particle filters construct effective proposal distributions by using invertible flow to migrate particles continuously from the prior distribution to the posterior, and sequential Markov chain Monte Carlo (SMCMC) methods use a Metropolis-Hastings (MH) accept-reject approach to improve filtering performance. In this paper, we propose to combine the strengths of invertible particle flow and SMCMC by constructing a composite Metropolis-Hastings (MH) kernel within the SMCMC framework using invertible particle flow. In addition, we propose a Gaussian mixture model (GMM)-based particle flow algorithm to construct effective MH kernels for multi-modal distributions. Simulation results show that for high-dimensional state estimation example problems the proposed kernels significantly increase the acceptance rate with minimal additional computational overhead and improve estimation accuracy compared with state-of-the-art filtering algorithms.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Li, Yunpengyunpeng.li@surrey.ac.uk
Pal, Soumyasundar
Coates, Mark J.
Date : 1 May 2019
DOI : 10.1109/TSP.2019.2905816
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Diane Maxfield
Date Deposited : 18 Jul 2019 11:48
Last Modified : 19 Sep 2019 15:15
URI: http://epubs.surrey.ac.uk/id/eprint/852276

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