Recursive prediction error methods for online estimation in nonlinear state-space models

Abstract

Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important to the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy. >