Share Email Print
cover

Proceedings Paper

Multichannel blind deconvolution using low rank recovery
Author(s): Justin Romberg; Ning Tian; Karim Sabra
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

We introduce a new algorithm for multichannel blind deconvolution. Given the outputs of K linear time- invariant channels driven by a common source, we wish to recover their impulse responses without knowledge of the source signal. Abstractly, this problem amounts to finding a solution to an overdetermined system of quadratic equations. We show how we can recast the problem as solving a system of underdetermined linear equations with a rank constraint. Recent results in the area of low rank recovery have shown that there are effective convex relaxations to problems of this type that are also scalable computationally, allowing us to recover 100s of channel responses after a moderate observation time. We illustrate the effectiveness of our methodology with a numerical simulation of a passive noise imaging" experiment.

Paper Details

Date Published: 29 May 2013
PDF: 6 pages
Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500E (29 May 2013); doi: 10.1117/12.2018550
Show Author Affiliations
Justin Romberg, Georgia Institute of Technology (United States)
Ning Tian, Georgia Institute of Technology (United States)
Karim Sabra, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8750:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
Harold H. Szu, Editor(s)

© SPIE. Terms of Use
Back to Top