전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제51권9호
  • /
  • Pages.148-156
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  • 2014
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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효율적인 부공간 추적에 의한 강인한 MVDR 적응 어레이

Robust MVDR Adaptive Array by Efficient Subspace Tracking

  • 최양호 (강원대학교 전자통신전공)
  • Choi, Yang-Ho (Dept. of Electronic and Communication Engineering, Kangwon National University)
  • 투고 : 2013.12.22
  • 심사 : 2014.08.28
  • 발행 : 2014.09.25
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초록

MVDR(minimum variance distortionless response) 적응 어레이에서 조향벡터(steering vector)에 에러가 있으면 원하는 신호(desired signal)도 감쇠되어 성능이 심하게 저하될 수 있다. 본 논문에서는 이러한 에러에 대응할 수 있는 계산이 간편한 기법을 제안한다. 제안한 방법에서는 DCB(doubly constrained beamforming) 원리에 기초한 최소화 문제의 해 벡터를 구하고, 이벡터를 부공간에 투사하여 새로운 조향벡터로 사용한다. 최소화 문제의 해결과 부공간 투사에 필요한 주 고유쌍(principal eigenpairs)은 PASTd(projection approximation subspace tracking with deflation)를 변형한 MPASTd(modified PASTd)에 의거하여 직접 상관행렬(correlation matrix)을 추정함이 없이 수신 데이터로부터 구해진다. 그리고 고유쌍 계산에 있어, 기존에 알려진 MPASTd를 개선해서 계산량을 절감하면서 효과적으로 구하는 방법을 제시한다. 제안한 적응어레이 기법은 상관행렬을 추정하고 이를 고유분해(eigendecomposition)하는 기존방식보다 계산량을 크게 줄이고 우수한 성능을 가질 수 있다.

In the MVDR (minimum variance distortionless response) adaptive array, its performance could be greatly deteriorated in the presence of steering vector errors as the desired signal is treated as an interference. This paper suggests an computationally simple adaptive beamforming method which is robust against these errors. In the proposed method, a minimization problem that is formulated according to the DCB (doubly constrained beamforming) principle is solved to find a solution vector, which is in turn projected onto a subspace to obtain a new steering vector. The minimization problem and the subspace projection are dealt with using some principal eigenpairs, which are obtained using a modified PASTd(projection approximation subspace tracking with deflation). We improve the existing MPASTd(modified PASTd) algorithm such that the computational complexity is reduced. The proposed beamforming method can significantly reduce the complexity as compared with the conventional ones directly eigendecomposing an estimate of the corelation matrix to find all eigenvalues and eigenvectors. Moreover, the proposed method is shown, through simulation, to provide performance improvement over the conventional ones.

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참고문헌

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