New finite-dimensional filters for parameter estimation of discrete-time linear Gaussian models

TitleNew finite-dimensional filters for parameter estimation of discrete-time linear Gaussian models
Publication TypeJournal Article
Year of Publication1999
AuthorsElliott, R. J., and V. Krishnamurthy
JournalAutomatic Control, IEEE Transactions on
Volume44
Pagination938 -951
Date Publishedmay.
ISSN0018-9286
Keywordsdiscrete time systems, discrete-time systems, econometric modeling, expectation maximization algorithm, filtering theory, finite-dimensional filters, Gaussian processes, Kalman filter, Kalman filters, linear dynamical systems, linear Gaussian models, linear systems, maximum likelihood estimation, multisensor signal enhancement, parameter estimation, recursive filters, speech processing, state space model, state-space methods
Abstract

The authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical system. Important advantages of their filter-based EM algorithm compared with the standard smoother-based EM algorithm include: 1) substantially reduced memory requirements, and 2) ease of parallel implementation on a multiprocessor system. The algorithm has applications in multisensor signal enhancement of speech signals and also econometric modeling

URLhttp://dx.doi.org/10.1109/9.763210
DOI10.1109/9.763210

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