DocumentCode
1503800
Title
Box Gaussian Mixture Filter
Author
Ali-Löytty, Simo Sakari
Author_Institution
Dept. of Math., Tampere Univ. of Technol., Tampere, Finland
Volume
55
Issue
9
fYear
2010
Firstpage
2165
Lastpage
2169
Abstract
This note presents the box Gaussian mixture filter (BGMF), which is an efficient filter for the systems with mainly linear measurements but enables utilizing highly nonlinear measurements. BGMF contains a new way to approximate the prior distributions with a Gaussian mixture, whose components have small covariances. In this note we present results on the weak convergence of BGMF. In simulations, we see that in our hybrid position example BGMF outperforms the conventional particle filter.
Keywords
Gaussian processes; Kalman filters; filtering theory; Gaussian distribution; box Gaussian mixture filter; extended Kalman filter; filter banks; filtering techniques; nonlinear measurements; Convergence; Filter bank; Filtering theory; Gaussian distribution; Gaussian noise; Noise measurement; Nonlinear filters; Particle filters; Statistics; Time measurement; Extended Kalman filter; Gaussian distribution; filter banks; filtering techniques; filtering theory;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.2010.2051486
Filename
5473096
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