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

The Institute of Electronics and Information Engineers (대한전자공학회)
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Compressive Sensing Reconstruction Based on the Quantization Constraint Sets

양자화 제한 집합에 기초한 컴프레시브 센싱 복구

  • Kim, Dong-Sik (Department of Electronics and Information Engineering, Hankuk University of Foreign Studies)
  • 김동식 (한국외국어대학교 전자정보공학부)
  • Published : 2009.09.25
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Abstract

In this paper, a convex optimization technique, which is based on the generalized quantization constraint (GQC), is proposed in the compressive sensing reconstruction using quantized measures. The set size of the proposed GQC can be controlled, and through extensive numerical simulations based on the uniform scalar quantizers, the CS reconstruction errors are improved by 3.4-3.6dB compared to the traditional QC method for the CS problems of m/klogn > 2.

본 논문에서는, 컴프레시브 센싱(compressive sensing, CS)에서 양자화된 측정을 사용하여 CS 복구(reconstruction)를 하는 경우에 일반화된 양자화 제한(generalized quantization constraint, GQC) 집합을 사용하여 convex 최적화를 수행하는 방법을 제안하였다. 제안한 GQC에서는 기존의 양자화 제한 집합의 크기를 조정할 수 있도록 하였으며, 균일 스칼라 양자기를 사용한 CS 복구의 모의실험을 통하여 m/klogn > 2 인 CS 문제에서, 기존의 QC 방법에 비하여 CS 복구의 에러에서 3.4-3.6dB의 성능 개선을 얻을 수 있었다.

Keywords

References

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