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Distributed compressive sensing for correlated information sources
- Authors
- Park, J.[Park, J.]; Hwang, S.[ Hwang, S.]; Yang, J.[ Yang, J.]; Bae, K.[ Bae, K.]; Ko, H.[Ko, H.]; Kim, D.K.[ Kim, D.K.]
- Issue Date
- 2017
- Publisher
- Springer Verlag
- Keywords
- Compressive sensing; Distributed source coding; Random projection; Sensor networks; Sparsity
- Citation
- Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, v.194 LNICST, pp.130 - 137
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
- Volume
- 194 LNICST
- Start Page
- 130
- End Page
- 137
- ISSN
- 1867-8211
- Abstract
- The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-knowledge on the correlation structure for arbitrarily correlated sparse signal. Numerical results verify that the propose algorithm reduces the required number of measurements for correlated sparse signal detection compared to the existing DCS algorithm. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.
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