压缩感知技术研究及应用

随着信息需求的提高,在超宽带信号处理领域中,奈奎斯特(Nyquist)采样所需硬件成本昂贵、采样获取效率低,且对数据存储先提取后压缩再传输,造成大量资源浪费。压缩感知(Compressive Sensing,CS)把采样和压缩放在一起进行,若信号在某个域上是稀疏的、压缩的,就可以低于奈奎斯特采样频率进行采样。对n维的离散信号取其中少数一些值处理,在接收端采用一些算法进行恢复。本文引入了压缩感知技术以降低系统对采样速率的要求,基于CS理论,介绍了CS三个关键技术:信号稀疏表示、测量矩阵设计、压缩感知重构算法,以及CS技术在具体领域中的应用。

Analysis of Theory and Technology Application of Compressive Sensing

With the information demand increasing, the method which based on Nyquist sampling is expensive and low efficiency in ultra wideband signal processing field. To extract before transmission data storage can cause a lot of waste resources. Compressive Sensing can make sampling and compression at the same time. The sampling frequency is far less than the Nyquist sampling frequency as long as the signal is sparse in a domain. It can deal with discrete signal directly and take a few values for processing from n dimension discrete signal. Some algorithm is used to recover on the receiving - end. A compressive sensing method is proposed in this paper to reduce the requirement of the system in sampling rate. Firstly, the CS basic theory is introduced and three key technologies are summarized ： the design of the measurement matrix, com- pressive sensing reconstruction algorithm and sparse representation of signals. Then the application of compressive sensing technology in specific areas is introduced.