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Blind deconvolution and compressed sensing

 
: Stöger, D.; Jung, P.; Krahmer, F.

:

Institute of Electrical and Electronics Engineers -IEEE-; Fraunhofer-Institut für Hochfrequenzphysik und Radartechnik -FHR-, Wachtberg:
4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016 : 19-23 September 2016, Aachen, Germany
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2920-4
ISBN: 978-1-5090-2921-1
S.24-27
International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa) <3, 2015, Pisa>
Englisch
Konferenzbeitrag
Fraunhofer HHI ()

Abstract
In this paper we consider the classical problem of blind deconvolution of multiple signals from its superposition, also called blind demixing and deconvolution. One is given a signal Σri=1 wi × xi = y ϵ RL which is the superposition of r unknown source signals {xi}ri=1 and convolution kernels {wi}ri=1 The goal is to reconstruct the vectors wi and xi, which are elements of known but random subspaces. The problem can be lifted into a low rank matrix recovery problem. We will discuss uniform as well as non-uniform recovery guarantees.

: http://publica.fraunhofer.de/dokumente/N-444915.html