Particle Gaussian mixture filters-II. (December 2018)

• Record Type: Journal Article
• Authors: Raihan, Dilshad; Chakravorty, Suman
• Is Part Of: Automatica. Volume 98(2018)
• Page Start: 341
• Page End: 349
• Publication Date: 2018-12
• Subjects: Estimation algorithms -- Nonlinear filters -- Machine learning -- Gaussian mixture models -- Multimodality -- Markov chain Monte Carlo -- State estimation -- Curse of dimensionality -- Parallel algorithms; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2018.07.024
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: In our previous work, we proposed a particle Gaussian mixture (PGM-I) filter for nonlinear estimation. The PGM-I filter uses the transition kernel of the state Markov chain to sample from the propagated prior. It constructs a Gaussian mixture representation of the propagated prior density by clustering the samples. The measurement data are incorporated by updating individual mixture modes using the Kalman measurement update. However, the Kalman measurement update is inexact when the measurement function is nonlinear and leads to the restrictive assumption that the number of modes remains fixed during the measurement update. In this paper, we introduce an alternate PGM-II filter that employs parallelized Markov Chain Monte Carlo (MCMC) sampling to perform the measurement update. The PGM-II filter update is asymptotically exact and does not enforce any assumptions on the number of Gaussian modes. The PGM-II filter is employed in the estimation of two test case systems. The results indicate that the PGM-II filter is suitable for handling nonlinear/non-Gaussian measurement update.