Multisensor information compression and reconstruction

Bing Du; Liang Liu; Jun Zhang

doi:10.1117/12.817902

13 April 2009 Multisensor information compression and reconstruction

Bing Du, Liang Liu, Jun Zhang

Proceedings Volume 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009; 73450P (2009) https://doi.org/10.1117/12.817902
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

In this paper, we propose a method of sampled data compression and reconstruction using the theory of distributed compressed sensing for wireless sensor network, in which the correlation between the sensors is considered for joint sparsity representation, compression and reconstruction of the signals. And incoherent random projection CS matrix in each sensor is as encoding matrix to generate compressed measurements for storing, delivering and processing. The reconstruction algorithm with both acceptable complexity and precision is developed for noise corrupted measurements by fully utilizing of correlations diversity. The simulation shows that the number of measurements only slightly larger than the sparsity of the sampled sensor data is needed for successful recovery.

Citation Download Citation

Bing Du, Liang Liu, and Jun Zhang "Multisensor information compression and reconstruction", Proc. SPIE 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009, 73450P (13 April 2009); https://doi.org/10.1117/12.817902

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