基于高阶统计量的数字调制方式识别
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摘要
近几十年来人们针对不同的调制信号提出了许多调制识别的新方法和新思想，本文在分析现有研究的基础上，借鉴了已有的特征提取方法，提出了基于信号高阶统计量的识别方法。本方法分别从信号包络的高阶矩和复基带信号的高阶累量着手，建立识别参数，依据识别参数区分调制类型。
与以往的基于高阶累量识别方法相比，本文用到了较高阶数的四、六阶累量建立识别参数，因为二阶累量受噪声影响而高阶累量不受噪声影响，所以，本文建立的识别参数可从理论上去除噪声的影响。文中大量的仿真结果验证了本方法的可行性。
本文也对基于信号包络高阶矩的识别方法进行了详细的理论推导和大量仿真，与以往的方法相比，它识别信号种类更多。
For the last decades, researchers have introduced many innovational methods to solve the problem of the modulation recognition for different kinds of modulation signals. Having analyzed the existing study and inspired by top researchers' methods that have been used in feature extraction, this paper proposes a digital modulation recognition method based on higher order statistics. This method deduces character parameters and distinguishes modulation formats on the ground of higher order moments of signal envelop and higher order cumulants of complex baseband signal respectively.
Compared with the former modulation recognition methods based on higher order statistics, a new method based on the fourth and sixth order cumulants of received signal for classifying digital modulation signals is proposed. Since Gaussian white noise has no influence on higher order cumulants of received signal but does on the second cumulants, we do not use second cumulants here. Therefore, the influence of gaussian white noise on the recognition parameters presented in this paper is wiped off in the theory aspect. Feasibility of the proposed method is verified by large amount of experiment results presented in this paper.
Also, a modulation recognition method based on the higher order moments of digital signal envelop is deducted in detail. Simulation results of this method show that it can distinguish many more kinds of modulation signals in comparison of the former methods.
与以往的基于高阶累量识别方法相比，本文用到了较高阶数的四、六阶累量建立识别参数，因为二阶累量受噪声影响而高阶累量不受噪声影响，所以，本文建立的识别参数可从理论上去除噪声的影响。文中大量的仿真结果验证了本方法的可行性。
本文也对基于信号包络高阶矩的识别方法进行了详细的理论推导和大量仿真，与以往的方法相比，它识别信号种类更多。
For the last decades, researchers have introduced many innovational methods to solve the problem of the modulation recognition for different kinds of modulation signals. Having analyzed the existing study and inspired by top researchers' methods that have been used in feature extraction, this paper proposes a digital modulation recognition method based on higher order statistics. This method deduces character parameters and distinguishes modulation formats on the ground of higher order moments of signal envelop and higher order cumulants of complex baseband signal respectively.
Compared with the former modulation recognition methods based on higher order statistics, a new method based on the fourth and sixth order cumulants of received signal for classifying digital modulation signals is proposed. Since Gaussian white noise has no influence on higher order cumulants of received signal but does on the second cumulants, we do not use second cumulants here. Therefore, the influence of gaussian white noise on the recognition parameters presented in this paper is wiped off in the theory aspect. Feasibility of the proposed method is verified by large amount of experiment results presented in this paper.
Also, a modulation recognition method based on the higher order moments of digital signal envelop is deducted in detail. Simulation results of this method show that it can distinguish many more kinds of modulation signals in comparison of the former methods.
引文
[1] J.E. Hipp,"Modulation Classification Based on Statistical Moments", Milcom'86, pp.20.2.1-20.2.6, 1986
[2] C.D.Chung,A.Polydoros, "Envelope-Based Classification Schemes For Continuous-Phase Binary Frequency-Shift-Keyed Modulations", Milcom'94, pp.796-800, 1994
[3] S.S. Soliman, S. -Z. Hsue, "Signal classification Using Statistical Moments", IEEE Trans.Comm, Vol.40, No.5, pp. 908-916, May, 1992
[4] C.Y. Huang, A. Polydoros, "Likelihood Methods for MPSK Modulation Classification", IEEE Trans. Comm., Vol.43, No.2/3/4, pp. 1493-1504, Feb/Mar/Apr, 1995
[5] B.P. Paris, G.C. Orsak, H. Chen, N. Warke, "Modulation Classification in Unknown Dispersive Environments", ICASSP'97, pp. 3853-3856, 1997
[6] 边肇祺等，模式识别，北京：清华大学出版社，1988
[7] A.K. Jain, Robert P. W. Dubin, J.C. Mao, "Statistical Pattern Recognition: A Review", IEEE Trans. PAMI, Vol.22, No.1, pp. 4-37, Jan, 2000
[8] Hsue S Z, Soliman S S. "Automatic modulation recognition of digitally modulated signals". MILCOM'89, 37.4.1-37.4.5.
[9] Hsue S Z, Soliman S S. "Automatic modulation classification using zero crossing". Radar and Signal Processing, IEE Proceedings, 1990, Vol. 137 pp. 459-464.
[10] Assaleh K, Farrell K, Mammone R J. "A new method of modulation classification for digitally modulated signals". MILCOM'92, Vol.2 pp. 712-716.
[11] Lu Mingquan, Xiao Xianci, and Li lemin, "Ar modeling based features extraction for multiple signals for modulation recognition," in Signal Processing Proceedings, 4th International Conference, 1998, vol.2 pp. 1385-1388.
[12] K.C.Ho, W.Prokopiw, and Y.T.Chan, "Modulation identification by the wavelets." in Proc, SPIE, 1993, Vol.2491 pp. 492-503.
[13] N P. Ta, "A wavelet packet approach to radio signal modulation classification," ICCS'94, Singapore, Vol.1 pp. 210-214.
[14] Ho K C, Liang Hong, "Identification of digital modulation types using the wavelet transform". MILCOM'99, Vol. 1 pp. 427-431.
[15] Sangwoo Cho, Chong Hyun Lee, Jooh wan chun, "Classification of digital modulations using the LPC," National Aerospace and Electronics Conference, Proceedings of the IEEE, 2000, pp. 774-778.
[16] Gardener W A, "Spectral Correlation of Modulated Signals: PART Ⅰ-Analog Modulation." IEEE Trans. Communication. 1987, Vol.35 pp. 584-594.
[17] Gardener W A, "Spectral Correlation of Modulated Signals: PART Ⅱ-Digital Modulation." IEEE Trans. Communication. 1987, Vol.35 pp.595-601.
[18] Gardener W A, Spooner C W, "Cyclic spectral analysis for signal detection and modulation recognition", MILCOM'98, Vol.2 pp. 419-424.
[19] Samir S.Soliman, Shue-Zen Hsue, "Signal classification using statistical moments", IEEE Trans. Communication, 1992,vol.40 pp. 908-916.
[20] Wei Dai, Wouzheng Wang, Jing Wang, "Joint Power Estimation and Modulation Classification Using Second-and Higher Statistics", Wireless Communications and networking Conference, 2002, IEEE, Vol. 1 pp. 155-158.
[21] Swami A, Sadler B M. Hierarchical digital modulation classification using cumulants, IEEE Trans on communication, Vol.48, No3, Mar 2000 pp. 416-429
[22] 陈卫东，杨绍全。利用累量不变量对MPSK信号分类，西安电子科技大学学报，2002，29(2)pp．229—232．
[23] 吕新政，魏平。利用高阶累积量实现数字调制信号的自动识别，电子对抗技术，Vol．19，No．6 Nov 2004
[24] 刘贵江。一种适用于数字调制信号的载波频率估计方法，系统工程与电子技术，Vol．26，No．12 Dec 2002．
[25] 张贤达编著 “现代信号处理”(第二版)清华大学出版社 2000．
[26] 郭梯云 杨家玮 李建东编著“数字移动通信”(修订本)第二版 人民邮电出版社2001．3．
[27] 胡延平。利用参数统计方法自动识别数字调制信号，通信学报，Vol．23，No．2 Feb 2002．
[2] C.D.Chung,A.Polydoros, "Envelope-Based Classification Schemes For Continuous-Phase Binary Frequency-Shift-Keyed Modulations", Milcom'94, pp.796-800, 1994
[3] S.S. Soliman, S. -Z. Hsue, "Signal classification Using Statistical Moments", IEEE Trans.Comm, Vol.40, No.5, pp. 908-916, May, 1992
[4] C.Y. Huang, A. Polydoros, "Likelihood Methods for MPSK Modulation Classification", IEEE Trans. Comm., Vol.43, No.2/3/4, pp. 1493-1504, Feb/Mar/Apr, 1995
[5] B.P. Paris, G.C. Orsak, H. Chen, N. Warke, "Modulation Classification in Unknown Dispersive Environments", ICASSP'97, pp. 3853-3856, 1997
[6] 边肇祺等，模式识别，北京：清华大学出版社，1988
[7] A.K. Jain, Robert P. W. Dubin, J.C. Mao, "Statistical Pattern Recognition: A Review", IEEE Trans. PAMI, Vol.22, No.1, pp. 4-37, Jan, 2000
[8] Hsue S Z, Soliman S S. "Automatic modulation recognition of digitally modulated signals". MILCOM'89, 37.4.1-37.4.5.
[9] Hsue S Z, Soliman S S. "Automatic modulation classification using zero crossing". Radar and Signal Processing, IEE Proceedings, 1990, Vol. 137 pp. 459-464.
[10] Assaleh K, Farrell K, Mammone R J. "A new method of modulation classification for digitally modulated signals". MILCOM'92, Vol.2 pp. 712-716.
[11] Lu Mingquan, Xiao Xianci, and Li lemin, "Ar modeling based features extraction for multiple signals for modulation recognition," in Signal Processing Proceedings, 4th International Conference, 1998, vol.2 pp. 1385-1388.
[12] K.C.Ho, W.Prokopiw, and Y.T.Chan, "Modulation identification by the wavelets." in Proc, SPIE, 1993, Vol.2491 pp. 492-503.
[13] N P. Ta, "A wavelet packet approach to radio signal modulation classification," ICCS'94, Singapore, Vol.1 pp. 210-214.
[14] Ho K C, Liang Hong, "Identification of digital modulation types using the wavelet transform". MILCOM'99, Vol. 1 pp. 427-431.
[15] Sangwoo Cho, Chong Hyun Lee, Jooh wan chun, "Classification of digital modulations using the LPC," National Aerospace and Electronics Conference, Proceedings of the IEEE, 2000, pp. 774-778.
[16] Gardener W A, "Spectral Correlation of Modulated Signals: PART Ⅰ-Analog Modulation." IEEE Trans. Communication. 1987, Vol.35 pp. 584-594.
[17] Gardener W A, "Spectral Correlation of Modulated Signals: PART Ⅱ-Digital Modulation." IEEE Trans. Communication. 1987, Vol.35 pp.595-601.
[18] Gardener W A, Spooner C W, "Cyclic spectral analysis for signal detection and modulation recognition", MILCOM'98, Vol.2 pp. 419-424.
[19] Samir S.Soliman, Shue-Zen Hsue, "Signal classification using statistical moments", IEEE Trans. Communication, 1992,vol.40 pp. 908-916.
[20] Wei Dai, Wouzheng Wang, Jing Wang, "Joint Power Estimation and Modulation Classification Using Second-and Higher Statistics", Wireless Communications and networking Conference, 2002, IEEE, Vol. 1 pp. 155-158.
[21] Swami A, Sadler B M. Hierarchical digital modulation classification using cumulants, IEEE Trans on communication, Vol.48, No3, Mar 2000 pp. 416-429
[22] 陈卫东，杨绍全。利用累量不变量对MPSK信号分类，西安电子科技大学学报，2002，29(2)pp．229—232．
[23] 吕新政，魏平。利用高阶累积量实现数字调制信号的自动识别，电子对抗技术，Vol．19，No．6 Nov 2004
[24] 刘贵江。一种适用于数字调制信号的载波频率估计方法，系统工程与电子技术，Vol．26，No．12 Dec 2002．
[25] 张贤达编著 “现代信号处理”(第二版)清华大学出版社 2000．
[26] 郭梯云 杨家玮 李建东编著“数字移动通信”(修订本)第二版 人民邮电出版社2001．3．
[27] 胡延平。利用参数统计方法自动识别数字调制信号，通信学报，Vol．23，No．2 Feb 2002．