DocumentCode :
817898
Title :
Approximate non-Gaussian filtering with linear state and observation relations
Author :
Masreliez, C.J.
Author_Institution :
University of Washington, Seattle, Washington, USA
Volume :
20
Issue :
1
fYear :
1975
fDate :
2/1/1975 12:00:00 AM
Firstpage :
107
Lastpage :
110
Abstract :
Two approaches to the non-Gaussian filtering problem are presented. The proposed filters retain the computationally attractive recursive structure of the Kalman filter and they approximate well the exact minimum variance filter in cases where either 1) the state noise is Gaussian or its variance small in comparison to the observation noise variance, or 2) the observation noise is Gaussian and the system is one step observable. In both cases, the state estimate is formed as a linear prediction corrected by a nonlinear function of past and present observations. Some simulation results are presented.
Keywords :
Kalman filtering; Linear systems, stochastic discrete-time; Nonlinear filtering; Recursive estimation; State estimation; Bayesian methods; Filtering; Gaussian noise; Kalman filters; Linear systems; Nonlinear filters; Predictive models; Smoothing methods; State estimation; Upper bound;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.1975.1100882
Filename :
1100882
Link To Document :
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