신호 부공간에 기초한 간단한 적응 어레이 및 성능분석

Signal-Subspace-Based Simple Adaptive Array and Performance Analysis

  • Choi, Yang-Ho (Dept. of Electronic and Communication Engineering, Kangwon National University)
  • 투고 : 2010.02.04
  • 발행 : 2010.11.25

초록

원하는 신호의 도래방향에 관한 정보를 이용하여 적응 어레이는 이 방향으로 빔 이득을 유지하면서 간섭신호를 제거한다. 신호 부공간에서 가중벡터를 조정하면 전체 공간에서 조정하는 방식에 비해 빠른 수렴속도를 가지며, 도래각 정보에서의 에러에 강인한 특성을 가진다. 그러나 공분산 행렬의 고유분해가 필요하고 이에 따른 계산이 복잡하다. 본 논문에서는 PASTd(projection approximation subspace tracking with deflation) 방식에 기초하여 계산이 간단한 신호 부공간에 기초한 적응어레이를 제시한다. 제시된 방식은 고유벡터가 직교하도록 원래의 PASTd를 변형해서 사용하고 있고, 직접 고유분해하는 방식과 동일한 성능을 가지면서 계산량을 크게 감소시킬 수 있다. 또한 신호 부공간 어레이의 SINR(signal-to-interference plus noise ratio)성능을 이론적으로 분석하여, 이의 동작특성을 고찰하였다.

Adaptive arrays reject interferences while preserving the desired signal, exploiting a priori information on its arrival angle. Subspace-based adaptive arrays, which adjust their weight vectors in the signal subspace, have the advantages of fast convergence and robustness to steering vector errors, as compared with the ones in the full dimensional space. However, the complexity of theses subspace-based methods is high because the eigendecomposition of the covariance matrix is required. In this paper, we present a simple subspace-based method based on the PASTd (projection approximation subspace tracking with deflation). The orignal PASTd algorithm is modified such that eigenvectora are orthogonal to each other. The proposed method allows us to significantly reduce the computational complexity, substantially having the same performance as the beamformer with the direct eigendecomposition. In addition to the simple beamforming method, we present theoretical analyses on the SINR (signal-to-interference plus noise ratio) of subspace beamformers to see their behaviors.

키워드

참고문헌

  1. L. C. Godara, "Application of antenna arrays to mobile communications-Part II: Beamforming and DOA considerations," Proc. IEEE, vol. 85, No. 8, pp. 1195-1247, Aug. 1997.
  2. L. Chang and C.-C. Yeh, "Performance of DMI and eigenspace-based beamformers," IEEE Trans. Antennas Propagat., vol. AP-40, pp. 1336-1347, Nov. 1992.
  3. W. D. D. Feldman and L. J. Griffith, "A projection approach for robust adaptive beamforming," IEEE Trans. Signal Process., vol. SP-42, pp. 867-876, Apr. 1994.
  4. S.-J. Yu and J.-H. Lee, "Statistical performance of eigenspace-based adaptive array beamformers," IEEE Trans. Antennas Propag., vol. 44, no. 5, pp. 665-671, May 1996. https://doi.org/10.1109/8.496252
  5. Y.-H. Choi, "Eigenstructure-based adaptive beamforming for coherent and incoherent interference cancellation," IEICE Trans. Commun., vol. E85-B, no. 3, pp. 633-640, Mar.
  6. B. Yang, "Projection approximation subspace tracking," IEEE Trans Signal Processing, vol. 44, no. 1, pp. 95-107, Jan. 1995.
  7. B. Noble and J. Daniel, Applied Linear Algebra. New Jersey: Prentice-Hall, 1988.
  8. M. Wax and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Trans. Acoust., Speech, Signal Process., vol. 33, no. 2, pp. 387-392, Apr. 1985. https://doi.org/10.1109/TASSP.1985.1164557