中国物理B ›› 2004, Vol. 13 ›› Issue (3): 329-334.doi: 10.1088/1009-1963/13/3/011

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Blind adaptive identification of FIR channel in chaotic communication systems

Tommy W.S. Chow1, K.T. Ng1, 王保云2   

  1. (1)Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China; (2)Department of Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • 收稿日期:2003-06-06 修回日期:2003-09-24 出版日期:2004-03-06 发布日期:2005-07-06
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No 69702008) and the grant from City University of Hong Kong.

Blind adaptive identification of FIR channel in chaotic communication systems

Wang Bao-Yun (王保云)a, Tommy W.S. Chowb, K.T. Ngb    

  1. a Department of Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; b Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
  • Received:2003-06-06 Revised:2003-09-24 Online:2004-03-06 Published:2005-07-06
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No 69702008) and the grant from City University of Hong Kong.

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摘要： In this paper we study the problem of blind channel identification in chaotic communications. An adaptive algorithm is proposed, which exploits the boundness property of chaotic signals. Compared with the EKF-based approach, the proposed algorithm achieves a great complexity gain but at the expense of a slight accuracy degradation. However, our approach enjoys the important advantage that it does not require the a priori information such as nonlinearity of chaotic dynamics and the variances of measurement noise and the coefficient model noise. In addition, our approach is applicable to the ARMA system.

关键词: chaotic communication, blind identification, LMS algorithm, extended Kalman filtering

Abstract: In this paper we study the problem of blind channel identification in chaotic communications. An adaptive algorithm is proposed, which exploits the boundness property of chaotic signals. Compared with the EKF-based approach, the proposed algorithm achieves a great complexity gain but at the expense of a slight accuracy degradation. However, our approach enjoys the important advantage that it does not require the a priori information such as nonlinearity of chaotic dynamics and the variances of measurement noise and the coefficient model noise. In addition, our approach is applicable to the ARMA system.

Key words: chaotic communication, blind identification, LMS algorithm, extended Kalman filtering

中图分类号:  (Communication using chaos)

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