Accession Number : AD0775868
Title : Convergent Identification Algorithms.
Descriptive Note : Technical rept. 1 Jun-1 Dec 73,
Corporate Author : COLORADO STATE UNIV FORT COLLINS DEPT OF ELECTRICAL ENGINEERING
Personal Author(s) : Perl,Joseph ; Graupe,Daniel D. ; Scharf,Louis L.
Report Date : 01 MAR 1974
Pagination or Media Count : 134
Abstract : A least-squares algorithm is derived for memoryless system identification. Then a stochastic approximation algorithm is developed for identifying mixed auto regressive moving average (ARMA) processes. Since the correct auto regressive (AR) model is in general of infinite order, errors appear in an otherwise consistent estimation procedure. Upper bounds of these errors are developed for the ARMA parameters and for the Kalman-Bucy filter based on these identified parameters. Finally, an adaptive array estimation algorithm is developed for the case of correlated signal and noise fields and shown to converge in mean-square. (Modified author abstract)
Descriptors : *Control theory, *Kalman filtering, Identification, Stochastic processes, Signal processing, Noise, Adaptive control systems, Least squares method, Correlation techniques, Algorithms
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE