Compressed Sensing Theory Method Analysis

Compressive sensing is a new theory of information acquisition,which breaks through the traditional sampling theory,combines data acquisition with data compression,then recovers the original singnal by reconstruction algorithm. In the traditional signal sampling process, according to Shannon-Nyqusit theorem,sampling rate must be not less than twice the highest frequency signal in order to avoid singnal distortion ,which would lead to a large number of sampling data,reducing data processing efficiency. Compressive sensing employs the nonadaptive linear projection to obtain the original signal information, and then the signal reconstruction is conducted by using an numerical optimization problems from projection value.Compressive sensing makes the amount of data far less than that the traditional sampling theory needed.Therefore,compressive sensing theory is of much concern in the field of signal and image processing,and it also has a wide range of applications.Compressive sensing started late,there are many problems and research directions worthy of our in-depth research.At present,many researchers have focused on reconstruction algorithms.Reconstruction algorithm is the core of compressive sensing,which are of great significance to reconstructing compressed signals and verifying the accuracy in sampling.This paper introduces the basics of compressive sensing,and then study and analyze the existing reconstruction algorithms as the main content.Finally,the given signal and random signal,for example,we give a series of algorithms based on greedy algorithm and iterative hard thresholding algorithm for data implementation, algorithm analysis and experimental results.