基于正则化弱相关的分布式MWC重构算法

针对实际电磁环境中，信号稀疏度不易准确预知的难题，提出了基于正则化弱相关的分布式调制宽带转换器（Distributed modulated wideband converter, DMWC）重构算法，该算法不依赖稀疏度作为收敛条件。首先将满足弱相关性的原子加入索引集，然后正则化索引集，将新选出的原子加入支撑集。当残差能量达到阈值条件时，停止迭代。最后设置支撑集越界条件，删除支撑集中相关性较小的无效原子，得到最终的支撑集。仿真结果表明，本文算法能大大提高DMWC对信号传输衰减的容忍度。此外，在同等条件下，本文算法的恢复性能优于正交匹配追踪（Orthogonal matching pursuit,OMP）算法。

Distributed MWC Reconstruction Algorithm Based on Regularized Weak Correlation

Focused on the problem that the signal sparsity is difficult to be predicted accurately in actual electromagnetic environment, we propose a distributed modulated wideband converter (DMWC) reconstruction algorithm based on regularized weak correlation, which does not rely on the sparsity as convergence condition. First, the atoms that satisfy the weak correlation are added to the index set. Then, the index set is regularized and the newly selected atoms are added to the support set. When the residual energy reaches the threshold condition, the iteration is stopped. Finally, the support set out-of-bounds condition is set, and the invalid atoms with less correlation are deleted to obtain the final support set. Simulation results show that the proposed algorithm can greatly improve the tolerance of DMWC to signal transmission attenuation. In addition, under the same conditions, the recovery performance of this algorithm is better than orthogonal matching pursuit (OMP) algorithm.