A New Generalized Orthogonal Matching Pursuit Method

Abstract EN

To improve the reconstruction performance of the generalized orthogonal matching pursuit, an improved method is proposed.

Columns are selected from the sensing matrix by generalized orthogonal matching pursuit, and indices of the columns are added to the estimated support set to reconstruct a sparse signal.

Those columns contain error columns that can reduce the reconstruction performance.

Therefore, the proposed algorithm adds a backtracking process to remove the low-reliability columns from the selected column set.

For any k-sparse signal, the proposed method firstly computes the correlation between the columns of the sensing matrix and the residual vector and then selects s columns that correspond to the s largest correlation in magnitude and adds their indices to the estimated support set in each iteration.

Secondly, the proposed algorithm projects the measurements onto the space that consists of those selected columns and calculates the projection coefficient vector.

When the size of the support set is larger than k, the proposed method will select k high-reliability indices using a search strategy from the support set.

Finally, the proposed method updates the estimated support set using the selected k high-reliability indices.

The simulation results demonstrate that the proposed algorithm has a better recovery performance.