[1]李继楼,柯家龙. 基于压缩感知的WSN数据压缩与重构[J].计算机技术与发展,2015,25(09):111-114.
　LI Ji-lou,KE Jia-long. Data Compression and Recovery of WSN Based on Compressive Sensing[J].,2015,25(09):111-114.
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 基于压缩感知的WSN数据压缩与重构()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年09期
页码:
111-114
栏目:
智能、算法、系统工程
出版日期:
2015-09-10

文章信息/Info

Title:
 Data Compression and Recovery of WSN Based on Compressive Sensing
文章编号:
1673-629X（2015)09-0111-04
作者:
 李继楼; 柯家龙
 南京邮电大学 通信与信息工程学院
Author(s):
 LI Ji-lou; KE Jia-long
关键词:
 ; 无线传感网络; 压缩感知; 贝叶斯模型; 信号重构
Keywords:
 ; wireless sensor networks; compressive sensing; Bayesian model; signal reconstruction
分类号:
TP301
文献标志码:
A
摘要:
 在无线传感器网络( WSN)中，传统的处理方式是采用奈奎斯特技术对信号进行采样并重构，而随着信号频率的增加，应用奈奎斯特技术会使成本急剧增加，这是人们所不乐见的。针对这一问题，近年来出现一种新的技术即压缩感知技术，它能利用更少的数据和合适的重构方法得到更精确的原始信号。将稀疏贝叶斯学习( SBL)和压缩感知联合起来，形成了一种在噪声的情况下更好重建可压缩信号的方法，并进一步将这种方法应用在WSN中，可以在误差允许的范围内有效控制测量数据的维数，所以在保证了一定的误差的同时还减少了成本，提高了算法的效率。
Abstract:
 In wireless sensor networks,signal is sampled and reconstructed using the technology of Nyquist in the past. But it requires a substantial increase in the cost with the growth of the signal frequency,which is that people do not like to see. Recently a new technology is emerged,which is called compressive sensing technology,is a good way to solve this problem. Compressive sensing can use less data and appropriate reconstruction method to get a more accurate original signal. Put Sparse Bayesian Learning ( SBL) and compressive sens-ing together to form a better reconstruction compressible signal under the noise. This method can effectively control the dimension of measurement data within the range of allowed error in WSN,so you can ensure a certain degree of error while reducing the cost,impro-ving the efficiency of the algorithm.

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更新日期/Last Update: 2015-10-16