Abstract
Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We present a second new model for jointly sparse signals that allows for joint recovery of multiple signals from incoherent projections through simultaneous greedy pursuit algorithms. We also characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction.
Original language | English (US) |
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Title of host publication | Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers |
Pages | 1537-1541 |
Number of pages | 5 |
Volume | 2005 |
State | Published - Dec 1 2005 |
Event | 39th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Oct 28 2005 → Nov 1 2005 |
Other
Other | 39th Asilomar Conference on Signals, Systems and Computers |
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Country/Territory | United States |
City | Pacific Grove, CA |
Period | 10/28/05 → 11/1/05 |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications