Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
This addresses the recovery of finite-valued sparse signals, a problem with widespread applications, but appears incremental as it builds on existing compressed sensing frameworks.
The paper tackles the problem of recovering binary sparse signals from compressed linear measurements by proposing a novel polynomial optimization formulation, which is analyzed and compared to state-of-the-art binary compressed sensing methods.
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.