A compressive sensing framework for multirate signal estimation

This paper develops a novel compressive sensing setting for the multirate signal estimation problem. The multirate signal estimation task consists of estimating the values for a source signal when observed through several measurement channels sampled at different sampling rates. We demonstrate that this formulation can be recast in a compressive sensing setup. Reformulating the multirate signal estimation problem in a compressive sensing framework, enables us to infuse the sparse signal estimation and reconstruction methodologies into this multirate setting in a novel manner. We show that for sparse signals sampled through a multirate multichannel system, the compressive sensing signal reconstruction paradigm fits in effectively. Simulations are provided demonstrating that compressive sensing based signal reconstruction for multirate signal estimation is a viable and effective alternative.