| Issue |
ESAIM: Proc.
Volume 19, 2007
Conference Oxford sur les méthodes de Monte Carlo séquentielles
|
|
|---|---|---|
| Page(s) | 53 - 64 | |
| DOI | https://doi.org/10.1051/proc:071908 | |
| Published online | 30 October 2007 | |
The marginalized particle filter – analysis, applications and generalizations
Division of Automatic Control, Department of Electrical Engineering, Linköping University, Sweden SE–581 83 Linköping, Sweden
The marginalized 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 outlines the marginalized particle filter and very briefly 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.
© EDP Sciences, ESAIM, 2007
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