Convergent Identification Algorithms.
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)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1974
- Accession Number
- AD0775868
Entities
People
- Daniel D. Graupe
- Joseph Perl
- Louis L. Louis L. Scharf
Organizations
- Colorado State University