@inproceedings{9250c747a144435d8185a3e80ebcebf1,
title = "Multisensor information compression and reconstruction",
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.",
keywords = "Common component, Correlation, Distributed compressed sensing, Joint sparsity, Random projections, Reconstruction, Wireless sensor networks",
author = "Du Bing and Liu Liang and Zhang Jun",
year = "2009",
doi = "10.1117/12.817902",
language = "English",
isbn = "9780819476111",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Multisensor, Multisource Information Fusion",
note = "Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009 ; Conference date: 16-04-2009 Through 17-04-2009",
}