Cart (Loading....) | Create Account
Close category search window
 

General method for sinusoidal frequencies estimation using ARMA algorithms with nonlinear prediction error transformation

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
$31 $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

3 Author(s)
Platonov, A.A. ; Inst. of Electron. Fundamentals, Warsaw Univ. of Technol., Poland ; Gajo, Z.K. ; Szabatin, J.

A new general approach to estimating the frequencies of sinusoidal signals corrupted by an additive nonGaussian noise is presented. The mixture of sinusoids and noise is modeled by an autoregressive moving average (ARMA) model with nonGaussian model noise. A class of ARMA recursive algorithms with nonlinear prediction error transformation is proposed for frequencies estimation. For a given probability density function of the model noise, known except for the scale parameter, the presented method enables the derivation of the algorithms ensuring the fastest convergence of the covariance error matrix to the asymptotic one. The robust version of the algorithms is also discussed. The performance of the ARMA nonlinear algorithms is illustrated by simulation results

Published in:

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on  (Volume:5 )

Date of Conference:

23-26 Mar 1992

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.