Research Article
Laplace prior based distributed compressive sensing
@INPROCEEDINGS{10.4108/chinacom.2010.82, author={Liang Tang and Zheng Zhou and Lei Shi and Haipeng Yao and Jing Zhang and Yabin Ye}, title={Laplace prior based distributed compressive sensing}, proceedings={5th International ICST Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2011}, month={1}, keywords={Bayesian compressive sensing (BCS) Laplace prior statistically interrelationship distributed Bayesian compressive sensing}, doi={10.4108/chinacom.2010.82} }- Liang Tang
Zheng Zhou
Lei Shi
Haipeng Yao
Jing Zhang
Yabin Ye
Year: 2011
Laplace prior based distributed compressive sensing
CHINACOM
ICST
DOI: 10.4108/chinacom.2010.82
Abstract
Bayesian compressive sensing (BCS) utilizes the prior distribution of signal coefficients to reconstruct the original signal. The widely used prior is Laplace and Gaussian distributed. In this paper, we use the scene of L sets of signal sparse coefficients which are statistically related and take advantage of Laplace prior and statistically interrelationship among signals to propose the Laplace prior based distributed Bayesian compressive sensing. We provide the experiment result to demonstrating that the proposed method is an effective reconstruction algorithm and has a good performance.
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