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A Conjugate Gradient Algorithm for Blind Sensor Calibration in Sparse Recovery

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.
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Submitted on : Wednesday, October 9, 2013 - 2:20:12 PM
Last modification on : Thursday, January 20, 2022 - 4:20:42 PM
Long-term archiving on: : Friday, April 7, 2017 - 8:41:58 AM


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  • HAL Id : hal-00871323, version 1


Hao Shen, Martin Kleinsteuber, Cagdas Bilen, Rémi Gribonval. A Conjugate Gradient Algorithm for Blind Sensor Calibration in Sparse Recovery. IEEE International Workshop on Machine Learning for Signal Processing - 2013, Sep 2013, Southampton, United Kingdom. ⟨hal-00871323⟩



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