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

In this paper the authors derive a new class of finite-dimensional recursiv e 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 maximizatio n (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical system. Important advantages of their filter-based EM a lgorithm compared with the standard smoother-based EM algorithm include: 1) substantially reduced memory requirements and 2) ease of parallel implemen tation on a multiprocessor system. The algorithm has applications in multis ensor signal enhancement of speech signals and also econometric modeling.