压缩感知中的信号重构方法分析

摘要

压缩感知理论(CS)因高采样速率和巨大的存储空间被广泛应用于认知无线电中.重构算法是压缩感知理论的核心之一,也是目前的研究热点.介绍了压缩感知理论的基本模型和重构算法,在基本的梯度算法(GP)基础上做了改进,提出了巴兹莱一伯文(PBB)算法,并对两种重构算法进行了仿真.仿真结果表明,PBB算法能更好地重构信号.%Compressive sensing is widely used in cognitive radio because of its high sampling rate and huge storage space. Reconstruction algorithm is the key technique of compressive sensing and it is the studying hotspot at present. In this paper, the model of compressive sensing theory and reconstruction algorithm are introduced. In addition, a novel approach named projected Barzilai-Borwein (PBB) is exploited to better the performance of the basic gradient projection. The simulation illustrates that the PBB algorithm is better in reconstruction than GP.