多测量矢量稀疏重建算法的融合
Fusion of Sparse Reconstruction Algorithms for Multiple Measurement Vectors
摘要：
我们认为，从多个测量向量共享一个共同的支持稀疏信号的恢复。为此任务开发的几种算法的性能取决于参数，如稀疏信号的维数，测量向量的维数，稀疏水平，测量噪声。我们提出了一个融合框架，其中多个测量矢量重构算法参与和最终的信号估计是通过组合的信号估计的参与算法获得。我们提出了实现更好的重建性能比参与算法的条件。数值仿真表明，该融合算法往往优于参与算法。
英文摘要：
We consider the recovery of sparse signals that share a common support from multiple measurement vectors. The performance of several algorithms developed for this task depends on parameters like dimension of the sparse signal, dimension of measurement vector, sparsity level, measurement noise. We propose a fusion framework, where several multiple measurement vector reconstruction algorithms participate and the final signal estimate is obtained by combining the signal estimates of the participating algorithms. We present the conditions for achieving a better reconstruction performance than the participating algorithms. Numerical simulations demonstrate that the proposed fusion algorithm often performs better than the participating algorithms.
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