By Topic

ARMA spectral estimation of narrow-band processes via model reduction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
B. Wahlberg ; Dept. of Electr. Eng., Linkoping Univ., Sweden

The problem of estimating autoregressive moving average ARMA models for narrowband processes is considered. The following approach is proposed. Estimate a high-order autoregressive (AR) approximation of the process. By model reduction, based on a truncated internally balanced realization or optimal Hankel-norm model reduction, reduce the order of this high-order AR estimate to find a lower-order ARMA model. This algorithm gives ARMA spectral estimates with excellent resolution properties, without using iterative numerical minimization methods as for the maximum-likelihood method. How to take the narrowband assumption into account in the model reduction step is discussed in detail

Published in:

IEEE Transactions on Acoustics, Speech, and Signal Processing  (Volume:38 ,  Issue: 7 )