Abstract:
The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics....Show MoreMetadata
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.
Published in: IEEE Signal Processing Letters ( Volume: 26, Issue: 7, July 2019)