Local Basis Function Estimators for Identification of Nonstationary Systems

Abstract

The problem of identification of a nonstationary stochastic system is considered and solved using local basis function approximation of system parameter trajectories. Unlike the classical basis function approach, which yields parameter estimates in the entire analysis interval, the proposed new identification procedure is operated in a sliding window mode and provides a sequence of point (rather than interval) estimates. It is shown that for the polynomial basis all computations can be carried out recursively and that two important design parameters - the number of basis functions and the size of the local analysis window - can be chosen in an adaptive way.