By Topic

Maximum Likelihood Signal Classification using Second-Order Blind Deconvolution Probability Model

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

2 Author(s)
Gupta, M.R. ; University of Washington, Dept. of Electrical Engineering, Seattle, WA 98195 ; Anderson, H.S.

We address the problem of classifying a signal that has been corrupted by an unknown linear time-invariant filter. This problem is common in remote-sensing and non-destructive evaluation applications wheremultipath and spreading are prevalent. A traditional approach is blind deconvolution to estimate the original signal, followed by classification of the estimated signal. Blind deconvolution is an ill-posed estimation problem, and if only a classification is needed, then we hypothesize it is an unnecessary step. We present an alternative maximum likelihood classifier that uses second-order probability models for the original signal and the unknown corrupting filter. The resulting quadratic discriminant analysis classifier is shown to perform well over a range of signal-to-noise ratios for two different models of multipath, and in all cases performs consistently better than a standard blind deconvolution method followed by a quadratic discriminant analysis classifier.

Published in:

Statistical Signal Processing, 2007. SSP '07. IEEE/SP 14th Workshop on

Date of Conference:

26-29 Aug. 2007