NDLI: The Kalman Laplace filter: A new deterministic algorithm for nonlinear Bayesian filtering

Content Provider	IEEE Xplore Digital Library
Author	Bui Quang, P. Musso, C. Le Gland, F.
Copyright Year	2015
Description	Author affiliation: Inria Rennes Bretagne-Atlantique, Rennes, France (Le Gland, F.) \|\| DAM, CEA, Arpajon, France (Bui Quang, P.) \|\| Onera - The French Aerosp. Lab., Palaiseau, France (Musso, C.)
Abstract	We propose a new recursive algorithm for nonlinear Bayesian filtering, where the prediction step is performed like in the extended Kalman filter, and the update step is done thanks to the Laplace method for integral approximation. This algorithm is called the Kalman Laplace filter (KLF). The KLF provides a closed-form non-Gaussian approximation of the posterior density. The hidden state is estimated by the maximum a posteriori, using a dimension reduction method to alleviate the computation cost of the maximization. The KLF is tested on three simulated nonlinear filtering problems: target tracking with angle measurements, population dynamics monitoring, motion reconstruction by neural decoding. It exhibits a good performance, especially when the observation noise is small.
Starting Page	1566
Ending Page	1573
File Size	879845
Page Count	8
File Format	PDF
e-ISBN	9780982443866
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ISIF
Subject Keyword	Approximation methods Kalman filters Bayes methods Approximation algorithms Covariance matrices Computational modeling Hidden Markov models
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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