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Dictionary Design for Distributed Compressive Sensing
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DOI：10.1109/LSP.2014.2350024.png)
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Abstract
En 中文
Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity fora class of signals. In distributed compressive sensing (DCS), in addition to intra-signal correlation, inter-signal correlation is also exploited in the joint signal reconstruction, which goes beyond the aim of the conventional dictionary learning framework. In this letter, we propose a new dictionary learning framework in order to improve signal reconstruction performance in DCS applications. By capitalizing on the sparse common component and innovations (SCCI) model [1], which captures both intra-and inter-signal correlation, the proposed method iteratively finds a dictionary design that promotes various goals: i) signal representation; ii) intra-signal correlation; and iii) inter-signal correlation. Simulation results showthat our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs.
Keywords:
Compressive sensing
dictionary learning
distributed compressive sensing
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