Particle Gaussian mixture filters-I. (December 2018)

• Record Type: Journal Article
• Authors: Raihan, Dilshad; Chakravorty, Suman
• Is Part Of: Automatica. Volume 98(2018)
• Page Start: 331
• Page End: 340
• Publication Date: 2018-12
• Subjects: Estimation algorithms -- Nonlinear filters -- Gaussian mixture models -- Multimodality -- Particle filtering -- State estimation -- Machine learning -- Curse of dimensionality -- Kalman filters; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2018.07.023
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: In this paper, we propose a particle based Gaussian mixture filtering approach for nonlinear estimation that is free of the particle depletion problem inherent to most particle filters. We employ an ensemble of possible state realizations for the propagation of state probability density. A Gaussian mixture model (GMM) of the propagated uncertainty is then recovered by clustering the ensemble. The posterior density is obtained subsequently through a Kalman measurement update of the mixture modes. We prove the convergence in probability of the resultant density to the true filter density assuming exponential forgetting of initial conditions. The performance of the proposed filtering approach is demonstrated through several test cases and is extensively compared to other nonlinear filters.