DocumentCode :
834278
Title :
Interacting multiple model particle filter
Author :
Boers, Y. ; Driessen, J.N.
Volume :
150
Issue :
5
fYear :
2003
Firstpage :
344
Lastpage :
349
Abstract :
A new method for multiple model particle (nonlinear) filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.
Keywords :
Gaussian distribution; Markov processes; filtering theory; nonlinear filters; random noise; target tracking; Gaussian probability densities; IMM filter; Markovian switching systems; interacting multiple model filter; nonGaussian noise; nonlinear filter; nonlinearities; particle filter; target tracking;
fLanguage :
English
Journal_Title :
Radar, Sonar and Navigation, IEE Proceedings -
Publisher :
iet
ISSN :
1350-2395
Type :
jour
DOI :
10.1049/ip-rsn:20030741
Filename :
1249153
Link To Document :
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