Title | Finite dimensional smoothers for MAP state estimation of bilinear systems |
Publication Type | Journal Article |
Year of Publication | 1999 |
Authors | Johnston, L. A., and V. Krishnamurthy |
Journal | Signal Processing, IEEE Transactions on |
Volume | 47 |
Pagination | 2444 -2459 |
Date Published | sep. |
ISSN | 1053-587X |
Keywords | bilinear systems, EM algorithm, expectation-maximization algorithm, finite dimensional smoothers, finite-dimensional iterative algorithms, iterative methods, MAP state estimation, maximum a posteriori state sequence estimation, maximum likelihood sequence estimation, nonlinear filtering, nonlinear filters, nonlinear systems, optimal finite-dimensional solutions, recursive versions, smoothing methods, state estimation, state sequence estimation |
Abstract | In this paper, we present two finite-dimensional iterative algorithms for maximum a posteriori (MAP) state sequence estimation of bilinear systems. Bilinear models are appealing in their ability to represent or approximate a broad class of nonlinear systems. Our iterative algorithms for state estimation are based on the expectation-maximization (EM) algorithm and outperform the widely used extended Kalman smoother (EKS). Unlike the EKS, these EM algorithms are optimal (in the MAP sense) finite-dimensional solutions to the state sequence estimation problem for bilinear models. We also present recursive (on-line) versions of the two algorithms and show that they outperform the extended Kalman filter (EKF). Our main conclusion is that the EM-based algorithms presented in this paper are novel nonlinear filtering methods that perform better than traditional methods such as the EKF |
URL | http://dx.doi.org/10.1109/78.782188 |
DOI | 10.1109/78.782188 |