The Marginalized Particle Filter: Analysis, Applications and Generalizations
2006 (English)In: Proceedings of the 2006 Workshop on Sequential Monte Carlo Methods: filtering and other applications, 2006, 53-64 p.Conference paper (Refereed)
The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can beused when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper will briefly introduce the marginalized particle filter and hint at possible generalizations, giving rise to a larger family of marginalized nonlinear filters. Furthermore, we analyze several properties of the marginalized particle filter, including its ability to reduce variance and its computational complexity. Finally, we provide an introduction to various applications of the marginalized particle filter.
Place, publisher, year, edition, pages
2006. 53-64 p.
Nonlinear state estimation, Marginalized particle filter, Applications, Marginalized nonlinear filters
IdentifiersURN: urn:nbn:se:liu:diva-89243DOI: 10.1051/proc:071908OAI: oai:DiVA.org:liu-89243DiVA: diva2:607831
2006 Workshop on Sequential Monte Carlo Methods: filtering and other applications, Oxford, UK, July, 2006