Abstract
An OMP-like Covariance-Assisted Matching Pursuit (CAMP) method has recently been proposed. Given a priorknowledge of the covariance and mean of the sparse coefficients, CAMP balances the least squares estimator and the priorknowledge by leveraging the Gauss-Markov theorem. In this letter, we study the performance of CAMP in the framework of restricted isometry property (RIP). It is shown that under some conditions on RIP and the minimum magnitude of the nonzero elements of the sparse signal, CAMP with sparse level K can recover the exact support of the sparse signal from noisy measurements. l2 bounded noise and Gaussian noise are considered in our analysis.We also discuss the extreme conditions of noise (e.g. the noise power is infinite) to simply show the stability of CAMP.