Mixture Kalman Filters and Beyond
2015 (English)In: Current Trends in Bayesian Methodology with Applications / [ed] Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan, Boca Raton: Chapman and Hall/CRC Press, Taylor & Frances , 2015, 537-562 p.Chapter in book (Refereed)
The discrete time general state-space model is a flexible framework to deal with the nonlinear and/or non-Gaussian time series problems. However, the associated (Bayesian) inference problems are often intractable. Additionally, for many applications of interest, the inference solutions are required to be recursive over time. The particle filter (PF) is a popular class of Monte Carlo based numerical methods to deal with such problems in real time. However, PF is known to be computationally expensive and does not scale well with the problem dimensions. If a part of the state space is analytically tractable conditioned on the remaining part, the Monte Carlo based estimation is then confined to a space of lower dimension, resulting in an estimation method known as the Rao-Blackwellized particle filter (RBPF).
In this chapter, we present a brief review of Rao-Blackwellized particle filtering. Especially, we outline a set of popular conditional tractable structures admitting such Rao-Blackwellization in practice. For some special and/or relatively new cases, we also provide reasonably detailed descriptions.We confine our presentation mostly to the practitioners’ point of view.
Place, publisher, year, edition, pages
Boca Raton: Chapman and Hall/CRC Press, Taylor & Frances , 2015. 537-562 p.
Rao-Blackwellized particle filter; sequential Monte Carlo; marginalized particle filter
IdentifiersURN: urn:nbn:se:liu:diva-118279DOI: 10.1201/b18502-27ISBN: 978-1-4822-3511-1ISBN: 978-1-4822-3512-8OAI: oai:DiVA.org:liu-118279DiVA: diva2:813818
FunderLinnaeus research environment CADICSSwedish Foundation for Strategic Research
This is a review article on the models admitting Rao-Blackwellization in particle filtering.2015-05-252015-05-252015-09-22Bibliographically approved