New clustering methods are proposed to develop novel particle filters with Gaussian mixture models (PFGMM). In the PFGMM, the propagated samples are clustered to recover a Gaussian mixture model (GMM) using a clustering algorithm, which plays a fundamental role in the filter’s performance. Two clustering methods are introduced that simultaneously minimize the covariance of each of the GMM components and maximize the likelihood function. Under the scenarios considered in this article, it is shown through numerical simulation that the PFGMMs with the proposed clustering algorithms lead to better performance than the PFGMM employing the K-means or the expectation-maximization algorithms as well as the regularized particle filter (PF) and the Gaussian sum PF.
Clustering Methods for Particle Filters With Gaussian Mixture Models
IEEE Transactions on Aerospace and Electronic Systems ; 58 , 2 ; 1109-1118
2022-04-01
1205697 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Distributed Cubature Gaussian Mixture Filters
AIAA | 2017
|British Library Online Contents | 2015
|Rao-Blackwellized particle filtering with Gaussian mixture models for robust visual tracking
British Library Online Contents | 2014
|