Discrete-time estimation of nonlinear continuous-time stochastic systems

Abstract

In this paper we consider the problem of state estimation of a dynamic system whose evolution is described by a nonlinear continuous-time stochastic model. We also assume that the system is observed by a sensor in discrete-time moments. To perform state estimation using uncertain discrete-time data, the system model needs to be discretized. We compare two methods of discretization. The first method uses the classical forward Euler method. The second method is based on the continuous-time simulation of the deterministic part of the nonlinear system between consecutive times of measurement. For state estimation we apply an unscented Kalman Filter, which-as opposed to the well known Extended Kalman Filter-does not require calculation of the Jacobi matrix of the nonlinear transformation associated with this method.