DocumentCode
1790794
Title
Rao-Blackwellized particle filter for Markov modulated nonlinear dynamic systems
Author
Saha, Simanto ; Hendeby, Gustaf
Author_Institution
Dept. Electr. Eng., Linkoping Univ., Linköping, Sweden
fYear
2014
fDate
June 29 2014-July 2 2014
Firstpage
272
Lastpage
275
Abstract
The Markov modulated (switching) state space is an important model paradigm in statistical signal processing. In this article, we specifically consider Markov modulated nonlinear state-space models and address the online Bayesian inference problem for such models. In particular, we propose a new Rao-Blackwellized particle filter for the inference task which is our main contribution here. A detailed description of the problem and an algorithm is presented.
Keywords
Bayes methods; Markov processes; nonlinear dynamical systems; particle filtering (numerical methods); state-space methods; Markov modulated nonlinear dynamic systems; Rao-Blackwellized particle filter; online Bayesian inference problem; statistical signal processing; switching state space; Aircraft; Hidden Markov models; Markov processes; Nonlinear dynamical systems; Signal processing; State-space methods; Switches; Jump Markov nonlinear systems; Markov regime switching; Rao-Blackwellized particle filter; switching nonlinear state space;
fLanguage
English
Publisher
ieee
Conference_Titel
Statistical Signal Processing (SSP), 2014 IEEE Workshop on
Conference_Location
Gold Coast, VIC
Type
conf
DOI
10.1109/SSP.2014.6884628
Filename
6884628
Link To Document