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 letter, 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.

Locations

• IEEE Signal Processing Letters - View
• arXiv (Cornell University) - View - PDF
• DataCite API - View