Normalized regularized orthogonal matching pursuit algorithm

The idea of regularized orthogonal matching pursuit(ROMP) algorithm is to select multiple orthogonal column vectors at each iteration. Once chosen by mistake, the vectors can´t be deleted from the support set, so that the algorithm can´t be applied to signals with large sparity. In view of this problem, an improved regularized orthogonal matching pursuit algorithm is proposed in this paper. A factor is introduced in the improved algorithm before the iteration. First, the compression measurement matrix is transformed into a column vector. Secondly, the maximum correlation column vectors are detected by finding the location of the largest sum of elements of the column vector. Finally, the residuals and spectrum support are updated. The simulation experiments show that the improved algorithm effectively reduces the reconstruction error and running time, while it greatly improves the reconstruction rate of the large sparse signals.