New finite-dimensional filters for parameter estimation of discrete-time linear Gaussian models
نویسندگان
چکیده
In this paper 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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ورودعنوان ژورنال:
- IEEE Trans. Automat. Contr.
دوره 44 شماره
صفحات -
تاریخ انتشار 1999