NDLI: A simulation based algorithm for optimal quantization in non-linear/non-Gaussian state-space models

Content Provider	IEEE Xplore Digital Library
Author	Tadic, V.B. Doucet, A.
Copyright Year	2004
Description	Author affiliation: Dept. of Autom. Control & Syst. Eng., Sheffield Univ., UK (Tadic, V.B.; Doucet, A.)
Abstract	The problem of the approximation of the optimal filter for non-linear/non-Gaussian state-space models is considered in this paper. This problem is studied for models with a multi-dimensional (continuous) state space and one-dimensional (continuous) observation space. An approximation of the optimal filter based on quantization is proposed. We quantize both the state and observation processes to obtain a hidden Markov model with discrete state and observation spaces for which the optimal filter can be computed exactly. The problem of the optimal selection of the parameters of this approximating model (quantization thresholds, states, transition probabilities, likelihood probabilities) is considered. An algorithm based on Monte Carlo gradient estimation and stochastic approximation is proposed. The asymptotic properties of the proposed algorithm are analyzed and sufficient conditions for its convergence are also obtained.
Sponsorship	American Automatic Control Council IFAC
Starting Page	5408
Ending Page	5413
File Size	366813
Page Count	6
File Format	PDF
ISBN	0780383354
ISSN	07431619
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Quantization Hidden Markov models Filters State-space methods Approximation algorithms Monte Carlo methods Stochastic processes Algorithm design and analysis Sufficient conditions Convergence
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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