Optimal MAP estimation of bilinear systems via the EM algorithm

TitleOptimal MAP estimation of bilinear systems via the EM algorithm
Publication TypeConference Paper
Year of Publication1998
AuthorsKrishnamurthy, V., L. Johnston, and A. Logothetis
Conference NameAcoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Pagination2373 -2376 vol.4
Date Publishedmay.
Keywordsbilinear systems, EM algorithm, expectation-maximization algorithm, extended Kalman filter, finite dimensional iterative algorithm, general bilinear model, iterative algorithm, iterative methods, Kalman filters, Kalman smoothers, KSEM algorithm, maximum a posteriori state estimation, maximum likelihood estimation, nonlinear systems, optimal MAP estimation, optimisation, sequential estimation, signal processing, smoothing methods, state estimation, state sequence estimation

We present a finite dimensional iterative algorithm for optimal maximum a posteriori (MAP) state estimation of bilinear systems. Bilinear models are appealing in their ability to represent or approximate a broad class of nonlinear systems. We show that several bilinear models previously considered in the literature are special cases of the general bilinear model we propose. Our iterative algorithm for state estimation is based on the expectation-maximization (EM) algorithm and outperforms the widely used extended Kalman filter (EKF). Unlike the EKF our algorithm is an optimal (in the MAP sense) finite-dimensional solution to the state sequence estimation problem for bilinear models


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