한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

  • 제41권8호
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  • Pages.868-876
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  • 2016
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  • 1226-4717(pISSN)
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  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
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QR 반복법의 고유벡터를 이용한 수렴 판단 방법

Convergence Decision Method Using Eigenvectors of QR Iteration

  • 투고 : 2016.05.04
  • 심사 : 2016.07.11
  • 발행 : 2016.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

MUSIC(multiple signal classification) 알고리즘은 고유값(eigenvalue)과 고유벡터(eigenvector)를 이용하여 표적의 도래각을 추정하는 대표적인 알고리즘이다. 일반적으로 고유값과 고유벡터는 고유치 해석(eigen-analysis)을 이용하여 구할 수 있으나, 계산 복잡도가 높고 수렴 시간의 긴 문제점이 있다. 그러므로 저가형 실시간 시스템 구현에 한계가 있다. 이런 문제를 개선한 고유치 해석 방법으로 QR 반복법이 제안되었으나, 기존의 QR 반복법 수렴 판단 방법으로는 MUSIC 알고리즘 적용에 부적합하다는 한계가 있다. 본 논문에서는 QR 반복법의 고유치 기반의 기존 수렴 판단 방법의 문제점을 분석하고, 고유벡터를 활용한 개선된 수렴 판단 방법을 제안한다.

MUSIC (multiple signal classification) algorithm is a representative algorithm estimating the angle of arrival using the eigenvalues and eigenvectors. Generally, the eigenvalues and eigenvectors are obtained through the eigen-analysis, but this analysis requires high computational complexity and late convergence time. For this reason, it is almost impossible to construct the real-time system with low-cost using this approach. Even though QR iteration is considered as the eigen-analysis approach to improve these problems, this is inappropriate to apply to the MUSIC algorithm. In this paper, we analyze the problems of conventional method based on the eigenvalues for convergence decision and propose the improved decision algorithm using the eigenvectors.

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참고문헌

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