Recovery of binary sparse signals from compressed linear measurements via polynomial optimization

Abstract

The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse signals and we propose a novel formulation, based on polynomial optimization. This approach is analyzed and compared to the state-of-the-art binary compressed sensing methods.

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• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
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• Binary Compressive Sensing via Analog Fountain Coding
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Cited by in corpus (2)

• Chordal and factor-width decompositions for scalable semidefinite and polynomial optimization
• Phase transition in binary compressed sensing based on $L_1$-norm minimization