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

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2 Author(s)
Elliott, R.J. ; Dept. of Math. Sci., Alberta Univ., Edmonton, Alta., Canada ; Krishnamurthy, V.

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

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Automatic Control, IEEE Transactions on  (Volume:44 ,  Issue: 5 )