Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications

Blind Separation of Noisy Multivariate Data Using Second-Order Statistics: Remote-Sensing Applications

In this paper a second-order method for blind source separation of noisy instantaneous linear mixtures is presented for the case where the signal order k is unknown. Its performance advantages are illustrated by simulations and by application to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS...

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Bibliographic Details
Main Authors: Mueller, Amy V. (Contributor), Herring, Keith T. (Contributor), Staelin, David H. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers, 2010-03-09T19:04:04Z.
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