压缩传感的综述

A Survey on Compressive Sensing

传统采样中,采样频率需高于信号最高频率的2倍,依照该定理会需要海量的采样信号,给存储和传输带来了很大的麻烦。近年,压缩感知理论在数据采集方面带来了历史的飞跃。压缩传感主要依靠非自适应的线性投影的方法来维护信号的原结构,选用数值的最优化问题来重构原信号。因压缩传感在采样方面低于奈奎斯特频率,在许多领域有广阔的前景。

In the traditional sampling, the sampling frequency needs to be higher than two times the maximum frequency of the sig-nal. According to the theorem, a large amount of sampling signals will be needed, which brings a lot of trouble to storage and trans-mission. In recent years, the theory of compressed sensing has brought a historic leap in data collection. Compressive sensing main-ly relies on non-adaptive linear projection methods to maintain the original structure of the signal, and selects the numerical opti-mization problem to reconstruct the original signal. Since the compressive sensing is lower than the Nyquist frequency in sampling,it has broad prospects in many fields.