@inproceedings{ff82d6c13dfe4c749113be63e00e0294,
title = "A compressive sensing framework for multirate signal estimation",
abstract = "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.",
keywords = "Compressive sensing, Multirate systems, Signal estimation",
author = "Eksioglu, {Ender M.} and Tanc, {A. Karhan} and Kayran, {Ahmet H.}",
year = "2010",
doi = "10.1109/ISSPA.2010.5605572",
language = "English",
isbn = "9781424471676",
series = "10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010",
publisher = "IEEE Computer Society",
pages = "716--719",
booktitle = "10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010",
address = "United States",
}