@inproceedings{2a2b17b8325c43a688e52633594c5717,
title = "Texas hold 'em algorithms for distributed compressive sensing",
abstract = "This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its universality, computational asymmetry, tolerance to quantization and noise, and robustness to measurement loss. In this paper we propose recovery algorithms for the sparse common and innovation joint sparsity model. Our approach leads to a class of efficient algorithms, the Texas Hold 'Em algorithms, which are scalable both in terms of communication bandwidth and computational complexity.",
keywords = "Data compression, Multisensor systems, Signal reconstruction",
author = "Schnelle, {Stephen R.} and Laska, {Jason N.} and Chinmay Hegde and Duarte, {Marco F.} and Davenport, {Mark A.} and Baraniuk, {Richard G.}",
year = "2010",
doi = "10.1109/ICASSP.2010.5496168",
language = "English (US)",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2886--2889",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
address = "United States",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}