Abstract | ||
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Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. However, the existing DCS was proposed for a very limited ensemble of signals that has single common information \cite{Baron:2009vd}. In this paper, we propose a generalized DCS (GDCS) which can improve sparse signal detection performance given arbitrary types of common information which are classified into not just full common information but also a variety of partial common information. The theoretical bound on the required number of measurements using the GDCS is obtained. Unfortunately, the GDCS may require much a priori-knowledge on various inter common information of ensemble of signals to enhance the performance over the existing DCS. To deal with this problem, we propose a novel algorithm that can search for the correlation structure among the signals, with which the proposed GDCS improves detection performance even without a priori-knowledge on correlation structure for the case of arbitrarily correlated multi signal ensembles. |
Year | Venue | Field |
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2012 | CoRR | Mathematical optimization,Detection theory,Algorithm,Signal recovery,Theoretical computer science,Correlation,Compressed sensing,Mathematics |
DocType | Volume | Citations |
Journal | abs/1211.6522 | 1 |
PageRank | References | Authors |
0.37 | 5 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jeonghun Park | 1 | 5 | 1.94 |
Seunggye Hwang | 2 | 4 | 2.13 |
Janghoon Yang | 3 | 136 | 38.21 |
Dong Ku Kim | 4 | 245 | 60.39 |