By Topic

Adaptive M-estimators for use in structured and unstructured robust covariance estimation

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)
Brown, C.L. ; Signal Process. Group, Darmstadt Univ. of Technol. ; Brcich, Ramon F. ; Debes, C.

Covariance estimation is necessary in many applications such as source detection in array processing. Unfortunately, the sample covariance estimator is not robust. Here we investigate two broad approaches to robust covariance matrix estimation. The first is a model-free element-wise procedure, while the second is a structured approach based on pre-whitening. Both approaches utilize a robust one-dimensional scale estimator. It is the choice of this scale estimator and its effect on the overall covariance estimator that is the main purpose of this study. An adaptive M-estimator of scale is shown to have several advantages. Depending on the final comparison criterion, its use in a structured or element-wise covariance matrix estimator can lead to improved, robust performance

Published in:

Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on

Date of Conference:

17-20 July 2005