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
Link To Document :
