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
fDate :
June 29 2014-July 2 2014
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;
Conference_Titel :
Statistical Signal Processing (SSP), 2014 IEEE Workshop on
Conference_Location :
Gold Coast, VIC
DOI :
10.1109/SSP.2014.6884628
