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The Orthogonal Matching Pursuit (OMP) for compressed sensing iterates over ascheme of support augmentation and signal estimation. We present two novelmatching pursuit algorithms with intrinsic regularization of the signalestimation step that do not rely on a priori knowledge of the signal'ssparsity. An iterative approach allows for a hardware efficient implementationof our algorithm, and enables real-world applications of compressed sensing. Weprovide a series of numerical examples that demonstrate a good performance,especially when the number of measurements is relatively small.

Orthogonal Matching Pursuit with Tikhonov and Landweber Regularization