Learning to Observation Matrices of Compressive Sensing

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.

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