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

Statistical analysis of a subspace method for bearing estimation without eigendecomposition

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 $31
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

2 Author(s)
Stoica, P. ; Dept. of Technol., Uppsala Univ., Sweden ; Soderstrom, T.

The paper studies the statistical properties of a subspace-based method for bearing estimation without eigendecomposition (BEWE). The BEWE large-sample variance is derived and shown to be bounded from below by the MUSIC large-sample variance. The drawback of being less accurate than MUSIC is balanced by BEWE's computational advantage. In addition, it is shown that, unlike MUSIC, BEWE can accommodate the case of spatially finitely-correlated sensor noise

Published in:

Radar and Signal Processing, IEE Proceedings F  (Volume:139 ,  Issue: 4 )

Date of Publication:

Aug 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.