Sparse Signal Recovery Through Regularized Orthogonal Matching Pursuit for WSNs Applications

Regularized Orthogonal Matching Pursuit (ROMP) is an important greedy algorithm possessing both characteristics - speed and easier implementation. In this paper, we report on the efficacy of the ROMP for signal recovery through compressed sensing. Real-life temperature measurements are recorded utilizing an experimental test bed. The performance of the sparse signal recovery mechanism is measured in terms of peak-signal-to-noise-ratio and root-mean-square-error. Numerical results obtained through computer simulation analysed with reference to are generalized Orthogonal Matching Pursuit (gOMP). It is shown through results that ROMP outperforms the gOMP in case, accuracy is of prime concern in signal recovery.