NDLI: Particle filtering with transformed weights

Content Provider	IEEE Xplore Digital Library
Author	Miguez, J. Koblents, E.
Copyright Year	2013
Description	Author affiliation: Dept. of Signal Theor. & Commun., Univ. Carlos III de Madrid., Leganés, Spain (Miguez, J.; Koblents, E.)
Abstract	Particle filters are simulation-based algorithms for computational inference in dynamical systems that have become very popular over the years in many areas of science and engineering. They are derived from Bayes' theorem and the technique of importance sampling (IS), which entails the approximation of probability measures by way of weighted random samples in the space of interest. As a consequence, particle filters suffer from problems related to the degeneracy of these weights, a limitation shared with other IS-based methods. In practice, the weight degeneracy implies that in some scenarios (typically when the dimension of the state space is high or when the likelihood function of the system is sharp) classical particle filters become numerically unstable and fail to converge. In this paper we investigate the application of a recently proposed technique, termed nonlinear importance sampling (NIS), to the design of particle filters. We show how the standard particle filter can be easily modified to incorporate transformed weights computed according to the NIS scheme, then provide a concise proof of convergence of the resulting algorithm, and finally present computer simulation results to illustrate the potential improvement in performance that can be attained.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	364
Ending Page	367
File Size	270436
Page Count	4
File Format	PDF
e-ISBN	9781467331463
DOI	10.1109/CAMSAP.2013.6714083
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-15
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Monte Carlo methods Atmospheric measurements Heuristic algorithms Particle measurements Standards Convergence
Content Type	Text
Resource Type	Article

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