Feedback particle filter for a continuous-time Markov chain

Author

Tao Yang ; Mehta, Prashant G. ; Meyn, Sean P.

Author_Institution

Coordinated Sci. Lab., Univ. of Illinois at Urbana-Champaign (UIUC), Urbana, IL, USA

fYear

2013

fDate

17-19 June 2013

Firstpage

6772

Lastpage

6777

Abstract

This paper concerns approximation of Wonham´s filter for estimating a continuous-time Markov chain, with continuous measurements corrupted by noise. The approximation is a new manifestation of the feedback particle filter (FPF) [15], [14], [13], a control-oriented approach for nonlinear filtering. A complete characterization of the feedback mechanism that defines the FPF is obtained, which leads to tractable algorithms for the nonlinear filtering problem, even for large state spaces. Numerical examples illustrate the application of these techniques.

Keywords

Markov processes; continuous time systems; feedback; noise; nonlinear filters; particle filtering (numerical methods); state-space methods; FPF; Wonham filter approximation; continuous measurements; continuous-time Markov chain estimation; control-oriented approach; feedback mechanism; feedback particle filter; noise; nonlinear filtering problem; state spaces; Approximation methods; Equations; Markov processes; Mathematical model; Radiation detectors; Standards; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2013

Conference_Location

Washington, DC

ISSN

0743-1619

Print_ISBN

978-1-4799-0177-7

Type

conf

DOI

10.1109/ACC.2013.6580903

Filename

6580903