Adaptive pole estimation

Author

Nehorai, Arye ; Starer, David

Author_Institution

Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA

Volume

38

Issue

5

fYear

1990

fDate

5/1/1990 12:00:00 AM

Firstpage

825

Lastpage

838

Abstract

AN adaptive algorithm is developed for online estimation of the poles of autoregressive (AR) processes. The method estimates the poles directly from the data without intermediate estimation of the AR coefficients or polynomial factorization. It converges rapidly, is computationally efficient, and attains the Cramer-Rao bound (CRB) asymptotically. A closed-form expression for the asymptotic CRB is provided. Convergence to the true solution is proved, and methods are discussed for extending the algorithm for use with more general (e.g. autoregressive moving-average) models. Numerical examples are presented to demonstrate the performance of the algorithm

Keywords

adaptive systems; convergence of numerical methods; filtering and prediction theory; matrix algebra; parameter estimation; poles and zeros; signal processing; statistical analysis; AR coefficients; ARMA models; Cramer-Rao bound; adaptive algorithm; autoregressive moving-average; autoregressive processes; closed-form expression; online estimation; pole estimation; prediction error; Adaptive algorithm; Closed-form solution; Least squares approximation; Polynomials; Recursive estimation; Robustness; Sensor arrays; Signal processing; Speech; Transfer functions;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.56028

Filename

56028