基于相干性框架的部分支集已知的信号重建

Signal Reconstruction with Known Partial SupportBased on Coherence Framework

文章使用l_(2)-αl_(2)(0<α≤1)最小化模型利用信号自身的先验支撑信息来重建高维稀疏信号。这是首篇基于相干性框架的部分支集已知的信号重建,重点讨论3种噪声(l_(2)有界噪声、Dantzig Selector噪声和脉冲噪声)情形下信号鲁棒恢复的充分条件和误差估计。

In this paper,l_(1)-αl_(2)(0<α≤1)minimization model is employed to reconstruct the high-dimensional sparse signals by the prior support information of the signal itself.It is the first paper on signal reconstruction with known partial support that is based on coherence framework.It focuses on the sufficient conditions and error estimation for robust signal recovery under three kinds of noise circumstance,including l_(2) bounded noise,Dantzig Selector noise and impulse noise.