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.
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Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02037515), and (2012R1A2A2A01047554).
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Park, J., Hwang, S., Yang, J., Bae, K., Ko, H., Kim, D.K. (2017). Distributed Compressive Sensing for Correlated Information Sources. In: Jung, J., Kim, P. (eds) Big Data Technologies and Applications. BDTA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-58967-1_15
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DOI: https://doi.org/10.1007/978-3-319-58967-1_15
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