A Conjugate Gradient Algorithm for Blind Sensor Calibration in Sparse RecoveryReport as inadecuate

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1 TUM - Technical University of Munich 2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE

Abstract : This work studies the problem of blind sensor calibration BSC in linear inverse problems, such as compressive sens- ing. It aims to estimate the unknown complex gains at each sensor, given a set of measurements of some unknown train- ing signals. We assume that the unknown training signals are all sparse. Instead of solving the problem by using con- vex optimization, we propose a cost function on a suitable manifold, namely, the set of complex diagonal matrices with determinant one. Such a construction can enhance numerical stabilities of the proposed algorithm. By exploring a global parameterization of the manifold, we tackle the BSC prob- lem with a conjugate gradient method. Several numerical experiments are provided to oppose our approach to the so- lutions given by convex optimization and to demonstrate its performance.

Keywords : Blind sensor calibration compressive sensing conjugate gradient algorithm

Author: Hao Shen - Martin Kleinsteuber - Cagdas Bilen - Rémi Gribonval -

Source: https://hal.archives-ouvertes.fr/


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