Title :
Finite dimensional smoothers for MAP state estimation of bilinear systems
Author :
Johnston, Leigh A. ; Krishnamurthy, Vikram
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
fDate :
9/1/1999 12:00:00 AM
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
Keywords :
bilinear systems; iterative methods; maximum likelihood sequence estimation; nonlinear filters; smoothing methods; state estimation; EM algorithm; MAP state estimation; bilinear systems; expectation-maximization algorithm; finite dimensional smoothers; finite-dimensional iterative algorithms; maximum a posteriori state sequence estimation; nonlinear filtering; nonlinear systems; optimal finite-dimensional solutions; recursive versions; state sequence estimation; Biological system modeling; Brain modeling; Electroencephalography; Filtering algorithms; Iterative algorithms; Kalman filters; Nonlinear systems; Sequences; Signal processing; State estimation;
Journal_Title :
Signal Processing, IEEE Transactions on