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從GAR模型參數(shù)提取特征的數(shù)字調制識別新方法

陸明泉,  肖先賜,  李樂民

陸明泉, 肖先賜, 李樂民. 從GAR模型參數(shù)提取特征的數(shù)字調制識別新方法[J]. 電子與信息學報, 1999, 21(2): 145-151.
引用本文: 陸明泉, 肖先賜, 李樂民. 從GAR模型參數(shù)提取特征的數(shù)字調制識別新方法[J]. 電子與信息學報, 1999, 21(2): 145-151.
Lu Mingquan, Xiao Xianci, Li Lemin. A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS[J]. Journal of Electronics & Information Technology, 1999, 21(2): 145-151.
Citation: Lu Mingquan, Xiao Xianci, Li Lemin. A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS[J]. Journal of Electronics & Information Technology, 1999, 21(2): 145-151.

從GAR模型參數(shù)提取特征的數(shù)字調制識別新方法

A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS

  • 摘要: 本文提出了一種從觀察序列的廣義自回歸(GAR)模型參數(shù)提取待識別信號的偽瞬時中心頻率和偽瞬時3dB帶寬特征,并利用神經(jīng)網(wǎng)絡分類器的數(shù)字調制識別新方法。這種方法充分利用了GAR模型良好的抗噪聲能力和神經(jīng)網(wǎng)絡優(yōu)異的模式分類能力,能有效地改善低SNR條件下的調制識別性能。計算機模擬結果證實了該方法具有很高的識別率和良好的穩(wěn)健性。
    Abstract: A new digital modulation recognition method is proposed which is based on the features extracted from generalized autoregressive (GAR) model parameters and the use of neural network classifier. Because of the better noise suppression capacity of the GAR model and the powerful pattern classification capacity of the neural network classifier, the new method can significantly improve the recognition performance in lower SNR. Computer simulations also show that the new method possesses higher recognition ratio and better robustness.
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  • Dominguez L V, Borrallo T M P, Garcia J P, Mezcua B R. A general approach to the automatic classification of radio communication signals.[J]. Signal Processing.1991,22:239-[2]Nandi A K, Azzouz E E. Automatic analogue modulation recognition, Signal Processing, 1995, 46: 221-222.[3]Azzouz E E, Nandi A K. Automatic identification of digital modulation types.[J]. Signal Processing.1995,47:55-[4]Lu Mingquan, Xiao Xianci, Li Lemin. Cylic spectral features based modulation recognition, Proc. ICCT96, Beijing: 1996, 792-795.[5]Ho H C, Prokopiw W, Chan Y T. Modulation identification by the wavelet transform, Proc. MILCOM95, San Diego, CA: 1995, 886-890.[6]Nandi A K, Azzouz E E. Modulation recognition using artificial neural networks, Signal Process-[7]ing, 1997, 56: 165-175.[8]Assalch K.[J].Farrell K, Mammone R J. A new method of modulation classification for digital modulation signals, Proc. MILCOM92, San Diego, CA.0712,1992:-[9]Tsao J, Shyu W-J. Generalized autoregressive spectral estimation, Proc. ASSP92, Vo1.V, San Francisco, CA: 1992, 449-452.[10]Kay S M. Modern Spectral Estimation: Theory and Application, Englewood Cliffs, NJ: Prentice-[11]Hall, 1987, Chapter 5.
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出版歷程
  • 收稿日期:  1997-09-11
  • 修回日期:  1998-06-08
  • 刊出日期:  1999-03-19

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