A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping

In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.