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| 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 |