Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Reweighted L1-Minimization via Support Detection

Support 검출을 통한 reweighted L1-최소화 알고리즘

  • Lee, Hyuk (School of Information and Communication, Korea University) ;
  • Kwon, Seok-Beop (School of Information and Communication, Korea University) ;
  • Shim, Byong-Hyo (School of Information and Communication, Korea University)
  • 이혁 (고려대학교 컴퓨터.전파통신공학과) ;
  • 권석법 (고려대학교 컴퓨터.전파통신공학과) ;
  • 심병효 (고려대학교 컴퓨터.전파통신공학과)
  • Received : 2010.10.06
  • Accepted : 2011.01.31
  • Published : 2011.03.25
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Abstract

Recent work in compressed sensing theory shows that $M{\times}N$ independent and identically distributed sensing matrix whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal with high probability even if $M{\ll}N$. In particular, it is well understood that the $L_1$-minimization algorithm is able to recover sparse signals from incomplete measurements. In this paper, we propose a novel sparse signal reconstruction method that is based on the reweighted $L_1$-minimization via support detection.

압축 센싱 (Compressed Sensing) 기술을 통해 $M{\times}N$ 측정 행렬의 원소들이 특정의 독립적인 확률 분포에서 뽑혀 identically 분포의 성질을 가지고 있을 때 $M{\ll}N$의 경우에도 스파스 (sparse) 신호를 높은 확률로 정확하게 복원할 수 있다. $L_1$-최소화 알고리즘이 불완전한 측정에 대해서도 스파스 (sparse) 신호를 복원할 수 있다는 것은 잘 알려진 사실이다. 본 논문에서는 OMP를 변형시킨 support 검출과 가중치 기법을 이용한 $L_1$-최소화 방법을 통하여 스파스 (sparse) 신호의 복원 성능을 향상시키는 알고리즘을 제안하고자 한다.

Keywords

References

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