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

The Institute of Electronics and Information Engineers (대한전자공학회)
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Affine Projection Algorithm for Subband Adaptive Filters with Critical Decimation and Its Simple Implementation

임계 데시메이션을 갖는 부밴드 적응필터를 위한 인접 투사 알고리즘과 간단한 구현

  • 최훈 (충북대학교 전자공학과) ;
  • 배현덕 (충북대학교 전기전자컴퓨터공학부)
  • Published : 2005.09.25
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Abstract

In application for acoustic echo cancellation and adaptive equalization, input signal is highly correlated and the long length of adaptive filter is needed. Affine projection algorithms, in these applications, can produce a good convergence performance. However, they have a drawback that is a complex hardware implementation. In this paper, we propose a new subband affine projection algorithm with improved convergence and reduced computational complexity. In addition, we suggest a good approach to implement the proposed method. In this method by applying polyphase decomposition, noble identity and critical decimation to the anne projection algorithm the number of input vectors for decorrelation can be reduced. The weight-updating formula of the proposed method is derived as a simple form that compared with the NLMS(normalized least mean square) algorithm by the reduced projection order The efficiency of the proposed algorithm for a colored input signal was evaluated by using computer simulations.

적응 음향반향 제거나 적응 등화와 같은 응용에서 입력신호의 상관도는 매우 높고 긴 길이의 적응 필터가 필요하다. 이러한 응용에서 인접투사 알고리즘은 좋은 수렴성능을 보이지만 적응 필터의 계수갱신을 위한 많은 계산량 문제로 하드웨어 구현이 복잡한 단점이 있다. 본 논문에서는 개선된 수렴속도와 계산량을 줄일 수 있는 새로운 부밴드 인접투사 알고리즘과 간단한 구현을 위한 방법을 제안한다. 이 방법에서는 인접투사 알고리즘에 다위상 분해, 노블아이덴티티 그리고 임계 데시메이션을 적용, 상관도 제거를 위해 사용되는 입력 신호 벡터의 수(적응필터의 투사차원)를 줄일 수 있다. 제안한 방법의 적응필터 계수 갱신식은 투사차원의 감소에 의해 NLMS 알고리즘과 비교될 만큼 간단한 형태로 유도된다. 제안한 방법의 효율을 상관도가 높은 입력신호를 사용하여 실험을 통해 평가하였다.

Keywords

References

  1. B. Widrow and S. D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1985
  2. S. Haykin, Adaptive Filter Theory, 4th Ed., NJ: Prentice-Hall, 2002
  3. K. Ozeki and T. Umeda, 'An Adaptive Filtering Algorithm using an Orthogonal Projection to an Affine Subspace and Its Properties,' Electron. Comm. Jap., vol 67-A, no. 5, pp. 19-27, 1984
  4. S. G. Sankaran and A. A. Beex, 'Convergence behavior of the affine projection algorithm,' IEEE Trans. Signal Proc., vol. 48, no. 4, pp. 1086-1097, Apr. 2000 https://doi.org/10.1109/78.827542
  5. S. L. Gay and J. Benesty, Acoustic Signal Processing for Telecommunication, Kluwer Academic Press, 2000
  6. M. Rupp, 'A family of adaptive filter algorithms with decorrelating properties,' IEEE Trans. Signal Proc., vol. 46, pp. 771-775, Mar. 1998 https://doi.org/10.1109/78.661344
  7. S. Werner and P. S. R. Diniz, 'Set-membership affine projection algorithm,' IEEE Signal Proc. Lett., vol. 8, no. 8, pp. 231-235, Aug. 2001 https://doi.org/10.1109/97.935739
  8. H. C. Shin and A. H. Sayed, 'Mean-square performance of a family of affine projection algorithms,' IEEE Trans. Signal Proc., vol. 52, no. 1, pp. 90-102, Jan. 2004 https://doi.org/10.1109/TSP.2003.820077
  9. S. L. Gay and S. Tavathia, 'The fast affine projection algorithm,' IEEE Proc. ICASSP 1995, vol. 5, Detroit, MI, pp. 3023-3026, May 1995 https://doi.org/10.1109/ICASSP.1995.479482
  10. M. Tanaka, S. Makino, and J. Kojima, 'A block exact fast affine projection algorithm,' IEEE Trans. Speech and Audio Proc., vol. 7, pp. 79-86, Jan. 1999 https://doi.org/10.1109/89.736333
  11. F. Albu and H. K. Kwan, 'Fast block exact Gauss-Seidel pseudo affine projection algorithm,' Electronics Lett. vol. 40, pp. 1451-1453, Oct. 2004 https://doi.org/10.1049/el:20046320
  12. A. Gilloire and M. Vetterli, 'Adaptive filtering in subbands with critical sampling: analysis, experiments, and application to acoustic echo cancellation,' IEEE Trans. on Signal Proc., vol. 40, pp. 1862-1875, Aug. 1992 https://doi.org/10.1109/78.149989
  13. P. P. Vaidyanathan, Multirate System and Filter Banks, Englewood Cliffs, NJ: Prentice-Hall, 1993
  14. S. S. Pradhan and V. U. Reddy, 'A new approach to subband adaptive filtering', IEEE Trans. Signal Proc., vol. 45, no. 3, pp. 655-664, Mar. 1999 https://doi.org/10.1109/78.747773
  15. M. R. Petraglia, R. G. Alves and P. S. R. Diniz, 'New structures for adaptive filtering in subbands with critical sampling', IEEE Trans. Signal Proc., vol. 48, no. 12, pp. 3316-3327, Dec. 2000 https://doi.org/10.1109/78.886995
  16. K. A. Lee and W. S. Gan, 'Improving convergence of the NLMS algorithm using constrained subband updates,' IEEE Signal Proc. Lett., vol. 11, no. 9, pp. 736-739, Sep. 2004 https://doi.org/10.1109/LSP.2004.833445
  17. Q. G. Liu, B. Champagne and K. C. Ho, 'On the use of a modified fast affine projection algorithm in subbands for acoustic echo cancelation,' IEEE Proc., Digital Signal Proc. Workshop, pp. 354-357, Sep. 1996 https://doi.org/10.1109/DSPWS.1996.555534
  18. E. Chau, H. Sheikhzadeh, and R. L. Berennan, 'Complexity reduction and regularization of a fast affine projection algorithm for oversampled subband adaptive filters,' IEEE Proc. ICASSP 2004, vol. 5, pp. V109-112, May 2004 https://doi.org/10.1109/ICASSP.2004.1327059
  19. R. G. Alves, J. A. Apolinario Jr. M. T. and Petraglia, 'Subband adaptive filtering with critical sampling using the data selective affine projection algorithm,' IEEE Proc. ICASSP 2004, vol. 3, pp. Ⅲ257-260, May 2004 https://doi.org/10.1109/ISCAS.2004.1328732
  20. K. Nishikawa and H. Kiya, 'New structure of affine projection algorithm using a novel subband adaptive system,' IEEE Signal Proc. Workshop Sig. Proc. Adv. in Wireless Comm., pp. 364-367, Mar. 2001 https://doi.org/10.1109/SPAWC.2001.923926
  21. E. K. P. Chong and S. H. Zak, An Introduction to Optimization, John Wiley & Sons, Inc., 1996