摘要
压缩感知理论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。针对测量值受噪声污染的含噪稀疏重构问题,提出了近似l0范数期望值最小化方法。该算法基本思想是将含噪稀疏重构问题转化为近似l0范数期望值最小化问题,并利用噪声的统计特征将随机最优化问题化简为常规的最优化问题,然后采用最速下降法求解。数值仿真表明,本文提出的方法具有更好的重构精度,且计算量较小。
Compressed Sensing（CS） is a new framework for simultaneous sensing and compression,and how to recover sparse signal form limited measurements is the key problem in CS.A fast and stable method,called Expectation Minimization of approximate norm（abr.EML0）,is proposed for sparse signal reconstruction with noisy measurements.The basic idea of the method is that sparse signal is recovered by minimizing the expectation of approximate norm,and then the expectation model by statistical character of noise is simplified so that the expectation model can be solved by the steepest descent method.Simulation results show that the proposed method provides better accuracy than existing methods at lower computational cost.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2012年第5期45-48,共4页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(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 recovery
basis pursuit
smoothed norm
