摘要
压缩感知理论(Compressed Sensing,CS)是对信号压缩的同时进行感知的新理论,而如何通过有限的测量值重构稀疏信号是压缩感知理论中的核心问题。针对稀疏信号的重构问题,提出了迭代平滑l0范数最小化算法。该算法首先利用上次迭代得到的稀疏解估计部份支撑集I,然后建立并求解基于支撑集I的平滑l0范数最小化问题,最后对以上两步迭代少数几次得到稀疏解。数值仿真表明,本文所提出的算法重构信号需要测量值数少于已有的算法,且计算速度较快。
Compressed Sensing （CS） is a new framework for simultaneous sensing and compression, and how to reconstruct sparse signal form limited measurements is the key problem in CS. In this paper, a novel method called herative Smoothed l0 -norm （ISLO） is proposed for sparse signal reconstruction. This method estimates a support set I from a crrent reconstruction and obtains a new reconstruction by solving the minimization problem based on the support set I, and it iterates these two steps for a small number of times. Simulation results show that the proposed method needs fewer measurements than existing methods, while needing the low computational cost.
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2012年第5期642-647,共6页
Journal of Astronautics
基金
国家自然科学基金(61072120)
新世纪优秀人才支持计划资助项目(NCET)
关键词
压缩感知
稀疏信号重构
基追踪
平滑l0范数
Compressed sensing
Sparse signal reconstruction
Basis pursuit
Smoothed l0 norm
