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基于小波變換的數(shù)字調(diào)制信號(hào)識(shí)別方法的研究

陳健,  闊永紅,  李建東,  馬玉寶

陳健, 闊永紅, 李建東, 馬玉寶. 基于小波變換的數(shù)字調(diào)制信號(hào)識(shí)別方法的研究[J]. 電子與信息學(xué)報(bào), 2006, 28(11): 2026-2029.
引用本文: 陳健, 闊永紅, 李建東, 馬玉寶. 基于小波變換的數(shù)字調(diào)制信號(hào)識(shí)別方法的研究[J]. 電子與信息學(xué)報(bào), 2006, 28(11): 2026-2029.
Chen Jian, Kuo Yong-hong, Li Jian-dong, Ma Yu-bao. Modulation Identification of Digital Signals with Wavelet Transform[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2026-2029.
Citation: Chen Jian, Kuo Yong-hong, Li Jian-dong, Ma Yu-bao. Modulation Identification of Digital Signals with Wavelet Transform[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2026-2029.

基于小波變換的數(shù)字調(diào)制信號(hào)識(shí)別方法的研究

Modulation Identification of Digital Signals with Wavelet Transform

  • 摘要: 該文介紹了一種基于小波分類特征的數(shù)字調(diào)制信號(hào)的識(shí)別方法，創(chuàng)新之處在于同時(shí)應(yīng)用了連續(xù)小波變換和多層小波分解兩種方法提取信號(hào)的特征，并且對(duì)于不同調(diào)制信號(hào)采用了不同的分類特征。算法實(shí)現(xiàn)時(shí)不需要進(jìn)行碼元周期估計(jì)以及同步時(shí)間估計(jì)，從而使分類器的設(shè)計(jì)變得簡單，判決準(zhǔn)則簡化，提高了運(yùn)算速度和識(shí)別率。
    Abstract: A new method of digital modulation identification with wavelet transform is introduced in this paper. There are two ways to get the characteristics. One is to get the local maximum with the continuous wavelet transform; the other is the multiresolution analysis. Both of them have been used. For different modulated signals, different characteristics have been used. Compared with others, the classifier is easy to realize and the decision is simple. It is not necessary to estimate the code period and the synchronization time. The percentage of correct identification is improved. The speed of modulation identification is increased as well.
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  • Liang Hong, Ho K C. Identification of digital modulation types using the wavelet transform[J].MILCOM 1999, Military Communications Conference Proceedings, 1999. IEEE, 31 Oct.-3 Nov.1999, Vol.1:427-431[2]Ho K C，Prokopiw W，Chan Y T. Modulation identification of digital signals by the wavelet transform[J].IEE Proc.-Radar, Sonar Navig.2000, 147(4):169-[3]Ta N P. A wavelet packet approach to radio signal classification. Time-Frequency and Time-Scale Analysis, 1994. Proceedings of the IEEE-SP International Symposium on 25-28 Oct., 1994: 508－511.[4]Jones E, Runkle P, Dasgupta N, et al.. Genetic algorithm wavelet design for signal classification[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2001, 23(8):890-[5]Zhang Q, Benveniste A. Wavelet network[J].Proc.IEEE Trans.on Neural Networks.1992, 3(6):889-[6]何正友, 錢清泉. 小波神經(jīng)網(wǎng)絡(luò)改進(jìn)結(jié)構(gòu)及其學(xué)習(xí)算法. 西南交通大學(xué)學(xué)報(bào), 1999, 34(4): 436－440.[7]張文紅, 韓剛, 李建東. 基于支撐矢量機(jī)的通信信號(hào)調(diào)制識(shí)別方法研究. 通信技術(shù), 2003, 134(2): 12－14.[8]Mallat S. A theory for multiresolution signal decomposition： The wavelet representation[J].IEEE Trans Pattern Analysis Macine Inteligcncel.1989, 11:674-693[9]楊力華等(譯). 信號(hào)處理的小波導(dǎo)引. 北京: 機(jī)械工業(yè)出版社, 2002.9.[10]Daubechies I. Orthonormal bases of compactly supported wavelets[J].Comm on Pure. Appl. Math.1988, 41:909-996[11]Panagiotou, P, Anastasopoulos, A, Polydoros A. likelihood ratio tests for modulation classification. MILCOM 2000, 21st Century Military Communications Conference Proceedings, 22-25 Oct., 2000, Vol.2: 670－674.
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出版歷程
  • 收稿日期:  2005-03-15
  • 修回日期:  2005-08-31
  • 刊出日期:  2006-11-19

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