Recursive Identification of Nonstationary Random Processes and Systems

Abstract

Identification of time-varying random processes and systems by adaptive filtering methods is considered. A method for the synthesis of a stochastic difference equation modeling the dynamics of unknown parameters is proposed. A priori information required for the construction of this equation consists of the choice of the basis of functions approximating time evolution of the parameters. A recursive algorithm for the model structure selection and for adaptive parameter estimation is presented. An example of parametric spectral analysis of nonstationary autoregressive process is given.