NDLI: Gaussian particle filtering

Content Provider	IEEE Xplore Digital Library
Author	Kotecha, J.H. Djuric, P.M.
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. & Comput. Eng., State Univ. of New York, Stony Brook, NY, USA (Kotecha, J.H.)
Abstract	Sequential Bayesian estimation for dynamic state space models involves recursive estimation of hidden states based on noisy observations. The update of filtering and predictive densities for nonlinear models with non-Gaussian noise using Monte Carlo particle filtering methods is considered. The Gaussian particle filter (GPF) is introduced, where densities are approximated as a single Gaussian, an assumption which is also made in the extended Kalman filter (EKF). It is analytically shown that, if the Gaussian approximations hold true, the GPF minimizes the mean square error of the estimates asymptotically. The simulations results indicate that the filter has improved performance compared to the EKF, especially for highly nonlinear models where the EKF can diverge.
Sponsorship	IEEE Signal Process. Soc
Starting Page	429
Ending Page	432
File Size	422516
Page Count	4
File Format	PDF
ISBN	0780370112
DOI	10.1109/SSP.2001.955314
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-08-08
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Recursive estimation Bayesian methods State estimation State-space methods Predictive models Monte Carlo methods Particle filters Gaussian approximation Mean square error methods
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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