A survey on compressive sensing

Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist Sampling Theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of compressive sensing. This paper gives survey of CS. It gives a brief background on the origin, reviews the basic mathematical foundation. The various reconstruction algorithms are also discussed. Finally, the survey concludes by discussing about the challenges of CS.