By Topic

Exploiting non-Gaussianity for signal separation

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

1 Author(s)
Clarke, I.J. ; DERA, Malvern, UK

The purpose of this paper is to contrast, in terms of standard criteria for statistical independence, cumulant-based methods of independent component analysis (ICA) with a potentially more robust blind signal separation technique called BLISS. This is able to separate independent non-Gaussian co-channel signals from multisensor data using only the joint probability distributions of instantaneous linear mixtures of those signals. BLISS is also able, without prior array calibrations or training waveforms, to estimate individual steering vectors including unknown mutual coupling and multipath. We point out fundamental reasons for the difficulty of comparing the performance of different ICA algorithms on finite duration practical data. We also propose a novel method for applying real-valued ICA to complex-valued data. By separately estimating in-phase and quadrature un-mixing parameters, we avoid the difficulty of selecting a subset of real and complex-valued cumulants. To justify our approach, we extend the definition of independence to the complex-valued case

Published in:

MILCOM 2000. 21st Century Military Communications Conference Proceedings  (Volume:2 )

Date of Conference: