Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems
Abstract
In this paper, a novel feedback control-based particle filter algorithm for the continuous-time stochastic hybrid system estimation problem is presented. This particle filter is referred to as the interacting multiple model-feedback particle filter (IMM-FPF), and is based on the recently developed feedback particle filter. The IMM-FPF is comprised of a series of parallel FPFs, one for each discrete mode, and an exact filter recursion for the mode association probability. The proposed IMM-FPF represents a generalization of the Kalmanfilter based IMM algorithm to the general nonlinear filtering problem. The remarkable conclusion of this paper is that the IMM-FPF algorithm retains the innovation error-based feedback structure even for the nonlinear problem. The interaction/merging process is also handled via a control-based approach. The theoretical results are illustrated with the aid of a numerical example problem for a maneuvering target tracking application.
Keywords
Cite
@article{arxiv.1305.5977,
title = {Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems},
author = {Tao Yang and Henk A. P. Blom and Prashant G. Mehta},
journal= {arXiv preprint arXiv:1305.5977},
year = {2013}
}