Rao-Blackwellized particle filter for Markov modulated nonlinear dynamic systems
2014 (English)In: Statistical Signal Processing (SSP), 2014 IEEE Workshop on, IEEE , 2014, 272-275 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2014. 272-275 p.
Rao-Blackwellized particle filter, Markov regime switching, switching nonlinear state space, Jump Markov nonlinear systems
IdentifiersURN: urn:nbn:se:liu:diva-107395DOI: 10.1109/SSP.2014.6884628ISI: 000361019700069ScopusID: 2-s2.0-84907396457ISBN: 9781479949755OAI: oai:DiVA.org:liu-107395DiVA: diva2:723835
IEEE statistical signal processing workshop (SSP 2014), Gold Coast, Australia, 29 June – 2 July 2014
FunderSwedish Research Council, CADICSSwedish Foundation for Strategic Research , COOPLOC