Compressive Sensing approach for data recovery from incomplete measurements for one dimensional signal

In various situations there is a need to measure and transmit fast moving data such as in Radar, Oceanography, Continuous Monitoring of Meteorological Parameters, etc. It may sometimes happen that some data gets lost. So to recover this information complete original data is to be resent. In this paper, we have discussed a new type of signal acquisition theory called as Compressive Sensing (CS). Signals having Sparse representation in one or the other domain can be faithfully reconstructed from the random undersampled measurements. Naturally occurring signals such as audio signals are having sparse nature in Fourier Domain. One such implementation of CS theory is also explained and demonstrated. Greedy Algorithm known as Orthogonal Matching Pursuit is used in recovery process.