The Marginalized Particle Filter - Analysis, Applications and Generalizations
2006 (English)In: Workshop on Sequential Monte Carlo Methods: filtering and other applications,2006, 2006Conference paper (Refereed)
particle filter is a powerful combination of the particle filter
and the Kalman filter, which can be used 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
Nonlinear state estimation, marginalized particle filter, applications, marginalized nonlinear filters
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-35408Local ID: 26665OAI: oai:DiVA.org:liu-35408DiVA: diva2:256256