基于压缩感知的数据压缩与检测

在无线传感器网络（ WSN）中，以往都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大幅度的增加，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，形成了一种在有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，在保证一定误差的同时还减少了成本，提高了算法的效率。

Data Compression and Detection Based on Compressive Sensing

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. Compressive sensing can use less data and appropriate reconstruction method to get a more accurate original signal. Put Sparse Bayesian Learning （ SBL） and compressive sensing together to form a better way of re-constructing 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,improving the efficiency of the algorithm.