CAJ | 학술논문

压缩感知可由少量观测重构K-稀疏信号.本文提出的极大熵方法克服了压缩感知中lp(0＜p＜1)最优化问题的非光滑性.极大熵方法构造一条同伦曲线以获得全局最优稀疏解.数值实验表明极大熵方法的信号重构性能优于l1最优化和AST算法.
압축감지가유소량관측중구K-희소신호.본문제출적겁대적방법극복료압축감지중lp(0＜p＜1)최우화문제적비광활성.겁대적방법구조일조동륜곡선이획득전국최우희소해.수치실험표명겁대적방법적신호중구성능우우l1최우화화AST산법.
Compressed Sensing (CS) can reconstruct K-sparse signal from remarkably few measurements.The paper provides a new maximum entropy function (MEF) method to overcome the nonsmooth problem of the nonconvex lp (0 ＜ p ＜ 1) optimization in CS.MEF constructs a homotopy path to obtain the global optimal sparse solution.The numerical results show that MEF has better performance of signal reconstruction than the l1 optimization and the affine scaling transformation algorithm.