Sparse signal recovery from noisy measurements via searching forward OMP

Recovering sparse signals from compressed measurements has received much attention in recent years. Considering that measurement errors always exist, an improved orthogonal matching pursuit (OMP) method which is called Searching Forward OMP (SFOMP), is proposed in this letter. The proposed SFOMP method is designed for compressive sensing and sparse signal recovery in the noisy environment. To improve the recovery performance, the SFOMP method incorporates a searching forward strategy to find the column leading to a minimum norm of residual error among the added candidates in each iteration. Numerical results show that, compared with other commonly used methods, this method provides a higher recovery signal-to-noise ratio, more accurate reconstruction of support set, and a competitive computational complexity with noisy measurements.