| Popis: |
A unified view of the area of joint sparse recovery is presented for the weighted L2,1 minimization. The support invariance transformation (SIT) is discussed to insure that the proposed scheme does not change the support of the sparse signal. The proposed weighted L2,1 minimization framework utilizes a support-related weighted matrix to differentiate each potential position, resulting in a favorable situation that larger weights are assigned at those positions where indices of the corresponding bases are more likely to be outside of the row support so that the solution at those positions are close to zero. Therefore, the weighted L2,1 minimization prefers to allot the received energy to those positions where indices of the corresponding bases are inside of the row support, which further improves the sparseness of the solution. The simulations demonstrate that the weighted L2,1 minimization reaches the strong recover threshold with lower SNR and fewer measurements. Keywords-Weighted L2,1 minimization; sparse signal reconstruction; multiple measurement vectors (MMV) |
