Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/929663

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Title
Sequential Bayesian filtering via minimum distortion quantization
Author/Creator
Goodwin, Graham C.;  Feuer, Arie;  Müeller, Claus
Institution
The University of Newcastle. Faculty of Science & Information Technology, School of Electrical Engineering and Computer Science
Description
Bayes Rule provides a conceptually simple, closed form, solution to the sequential Bayesian nonlinear filtering problem. The solution, in general, depends upon the evaluation of high dimensional multivariable integrals and is thus computationally intractable save in a small number of special cases. Hence some form of approximation is inevitably required. An approximation in common use is based upon the use of Monte Carlo sampling techniques. This general class of methods is referred to as Particle Filtering. In this paper we advocate an alternative deterministic approach based on the use of minimum distortion quantization. Accordingly we use the term Minimum Distortion Nonlinear Filtering (MDNF) for this alternative class of algorithms. Here we review the theoretical support for MDNF and illustrate its performance via simulation studies.
Relation
Three Decades of Progress in Control Sciences p. 203-213
Publisher Link
http://dx.doi.org/10.1007/978-3-642-11278-2_14
Date
2010
Publisher
Springer
Keyword(s)
minimum distortion nonlinear filtering;  bayes rule;  particle filtering;  engineering
Resource Type
book chapter
Identifier
http://hdl.handle.net/1959.13/929663
Identifier
ISBN:9783642112775
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"Sequential Bayesian filtering via minimum distortion quantization"
 
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