In this paper, the iterativereweightedleastsquares (IRLS) algorithm for sparse signal recovery with partially known support is studied. We establish a theoretical analysis of the IRLS algorithm by incorporating som...
详细信息
In this paper, the iterativereweightedleastsquares (IRLS) algorithm for sparse signal recovery with partially known support is studied. We establish a theoretical analysis of the IRLS algorithm by incorporating some known part of support information as a prior, and obtain the error estimate and convergence result of this algorithm. Our results show that the error bound depends on the best (s + k)-term approximation and the regularization parameter lambda, and convergence result depends only on the regularization parameter lambda. Finally, a series of numerical experiments are carried out to demonstrate the effectiveness of the algorithm for sparse signal recovery with partially known support, which shows that an appropriate q (0 < q < 1) can lead to a better recovery performance than that of the case q = 1.
An efficient and fast technique for designing L-p approximation tilters using the iterativereweightedleast-squares (IRLS) algorithm is proposed. This technique introduces an extra frequency response which implicitly...
详细信息
An efficient and fast technique for designing L-p approximation tilters using the iterativereweightedleast-squares (IRLS) algorithm is proposed. This technique introduces an extra frequency response which implicitly includes the weighting function such that the filter coefficients can be obtained with O(N-2) complexity.
Fast design of two-dimensional FIR filters in the least lp-norm sense is investigated in this brief. The design problem is first formulated in a matrix form and then solved by a matrix-based iterativereweightedleast...
详细信息
Fast design of two-dimensional FIR filters in the least lp-norm sense is investigated in this brief. The design problem is first formulated in a matrix form and then solved by a matrix-based iterative reweighted least squares algorithm. The proposed algorithm includes two loops: one for updating the weighting function and the other for solving the weighted leastsquares (WLS) subproblems. These WLS subproblems are solved using an efficient matrix-based WLS algorithm, which is an iterative procedure with its initial iterative matrix being the solution matrix in the last iteration, resulting in a considerable CPU-time saving. Through analysis, the new algorithm is shown to have a lower complexity than existing methods. Three design examples are provided to illustrate the high computational efficiency and design precision of the proposed algorithm.
暂无评论