Parameter estimation of a target-directed dynamic system model with switching states

摘要

In this paper, we describe an implementation of the extended Kalman filter (EkF) for joint state and parameter estimation for a target-directed, switching state-space nonlinear system model and compare its performance with a maximum-likelihood parameter estimation procedure based on the expectation-maximisation (EM) algorithm. The model parameters consist of the target one and the time-constant one.