A Generalized Class of Pseudomeasurements for Identifying Unknown Parameters of Linear Stochastic Systems

Abstract

A new class of pseudomeasurements for discrete-time stochastic systems are derived from continuous-time linear stochastic systems with unknown parameters by applying time-discretization and Taylor expansion. Utilizing these pseudomeasurements, we propose new iterative methods which estimate the states of the discrete-time stochastic systems and identify the unknown parameters simultaneously. A numerical example is given to show the effectiveness of the proposed approach.