| Blind adaptive identification of FIR channel in chaotic communication systems |
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| 作者姓名： | 王保云 Tommy W.S. Chow K.T. Ng |
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| 作者单位： | Department of Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China |
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| 基金项目： | 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.
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| 关 键 词： | 盲识别 混沌通信 LMS运算规则 混沌信号 |
| 收稿时间： | 6/6/2003 12:00:00 AM |
Blind adaptive identification of FIR channel in chaotic communication systems |
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| Wang Bao-Yun,Tommy W.S. Chow and K.T. Ng.Blind adaptive identification of FIR channel in chaotic communication systems[J].Chinese Physics B,2004,13(3):329-334. |
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| Authors: | Wang Bao-Yun Tommy WS Chow and KT Ng |
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| Institution: | Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China; Department of Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China |
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| 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. |
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| Keywords: | chaotic communication blind identification LMS algorithm extended Kalman filtering |
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