摘要
稀疏信号的快速优化恢复是压缩感知理论(Compressed Sensing,CS)研究的热点。讨论了参数选取对迭代加权l1范数优化算法恢复效果的影响,并将参数规则化过程引入到算法中,提出了带有参数规则化过程的迭代加权l1范数优化算法。最后通过数值实验,表明改进的算法较大程度地提升了对稀疏信号的恢复能力。
Rapid recovery of sparse signals is an important issue in compressed sensing.This paper discusses the parameter selection of the signal recovery algorithm via the iterative weighted l1 norm.A regularization strategy is introduced for the iterative weighted l1 norm to improve the stability of the recovery algorithm.Several numerical experiments are carried out to evaluate the improvement of the proposed algorithm.Numerical result shows that the proposed algorithm can obviously improve the accuracy and stability of sparse signal recovery.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第3期128-130,共3页
Computer Engineering and Applications
基金
国家自然科学基金No.60776795
国家教育部新世纪人才支持计划
西北工业大学科技创新基金~~
关键词
压缩感知
稀疏信号
参数规则化
信号恢复
compressed sensing
sparse signal
parameter regularization strategy
signal recovery
