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.
Original language | English (US) |
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Title of host publication | 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings |
Pages | 2886-2889 |
Number of pages | 4 |
DOIs | |
State | Published - Nov 8 2010 |
Event | 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States Duration: Mar 14 2010 → Mar 19 2010 |
Other
Other | 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 |
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Country | United States |
City | Dallas, TX |
Period | 3/14/10 → 3/19/10 |
Keywords
- Data compression
- Multisensor systems
- Signal reconstruction
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering