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2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

DOI: 10.1109/icassp.2014.6853755

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Compressed sensing with unknown sensor permutation

Proceedings article published in 2014 by Valentin Emiya, Antoine Bonnefoy, Laurent Daudet, Remi Gribonval
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Compressed sensing is the ability to retrieve a sparse vector from a set of linear measurements. The task gets more difficult when the sensing process is not perfectly known. We address such a problem in the case where the sensors have been permuted, i.e., the order of the measurements is unknown. We propose a branch-and-bound algorithm that converges to the solution. The experimental study shows that our approach always retrieves the unknown permutation, while a simple convex relaxation strategy almost always fails. In terms of its time complexity, we show that the proposed algorithm converges quickly with respect to the combinatorial nature of the problem.