Performance Comparison of OMP and IGE OMP with Various Measurement Matrices in Compressive Sensing

As the name suggests, compressive sensing (CS) is a method that does compression while sampling. CS follows the Sub-Nyquist sampling process. Hence, it provides an advantage over sampling hardware complexity at higher frequencies and data transmission with fewer samples than traditional Shannon-Nyquist theory. The main challenge involved in CS is reconstructing original information using Sub-Nyquist samples at the receiver. To achieve this goal choosing an appropriate measurement matrix for a particular reconstruction algorithm is vital in this process. Incremental Gaussian Elimination (IGE) OMP reconstruction algorithm and Orthogonal Matching Pursuit (OMP) algorithms are considered in this work for reconstructing the original sinusoidal signal. And detailed performance analysis is conducted for different measurement matrices used in the IGE OMP and OMP reconstruction process.