Learning to observation matrices of compressive sensing

Abstract

In this paper, a binary sparse observation matrix for compressive sensing is deterministically constructed via a pseudo-random sequence generated by the sub-shift mapping of finite type on the chaotic symbolic space. Analysis and experimental results demonstrate the proposed matrix's simplification can be regarded as a reliable method and is usable in compressive sensing applications. © 2012 Springer-Verlag GmbH.