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Dictionary design for distributed compressive sensing


Type

Article

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Authors

Chen, W 
Wassell, IJ 
Rodrigues, MRD 

Abstract

Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity for a 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 show that our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs.

Description

Keywords

Compressive sensing, dictionary learning, distributed compressive sensing

Journal Title

IEEE Signal Processing Letters

Conference Name

Journal ISSN

1070-9908
1558-2361

Volume Title

22

Publisher

Institute of Electrical and Electronics Engineers (IEEE)
Sponsorship
Engineering and Physical Sciences Research Council (EP/K033700/1)
This work is supported by EPSRC Research Grant EP/K033700/1 and EP/K033166/1, the Fundamental Research Funds for the Central Universities (No. 2014JBM149), the State Key Laboratory of Rail Traffic Control and Safety (RCS2012ZT014) of Beijing Jiaotong University, the Natural Science Foundation of China (U1334202), the Key Grant Project of Chinese Ministry of Education (313006).