Compressive sensing is a new type of digital signal processing method. The novel objective of compressive Sensing is to reconstruct a signal accurately and efficiently from far fewer sampling points got by Nyquist sampling theorem. Compressive sensing theory combines the process of sampling and compression to reduce the complexity of signal processing, which is widely used in many fields. so there are wide application prospects in the areas of radar image, wireless sensor network (WSN), radio frequency communication, medical image processing, image device collecting and so on. One of the important tasks in CS is how to recover the signals more accurately and effectively, which is concerned by many researchers.
Compressive sensing started late; there are many problems and research directions worthy of our in-depth research. At present, many researchers shove focused on reconstruction algorithms. Reconstruction algorithms are the core of compressive sensing, which are of great significance to reconstructing compressed signals and verifying the accuracy in sampling.
These papers introduce CosaMP algorithm; and then study and analyze the Gaussian noise as the main content. Finally, the given signal and random signal, for example, we give a series of comparison results.
Experimental Study on Sampling Theorem in Signal Processing
