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基于多重分形特征的通信調(diào)制方式識別研究

陳紅,  蔡曉霞,  徐云,  劉文濤

陳紅, 蔡曉霞, 徐云, 劉文濤. 基于多重分形特征的通信調(diào)制方式識別研究[J]. 電子與信息學(xué)報, 2016, 38(4): 863-869. doi: 10.11999/JEIT150703
引用本文: 陳紅, 蔡曉霞, 徐云, 劉文濤. 基于多重分形特征的通信調(diào)制方式識別研究[J]. 電子與信息學(xué)報, 2016, 38(4): 863-869. doi: 10.11999/JEIT150703
CHEN Hong, CAI Xiaoxia, XU Yun, LIU Wentao. Communication Modulation Recognition Based on Multi-fractal Dimension Characteristics[J]. Journal of Electronics & Information Technology, 2016, 38(4): 863-869. doi: 10.11999/JEIT150703
Citation: CHEN Hong, CAI Xiaoxia, XU Yun, LIU Wentao. Communication Modulation Recognition Based on Multi-fractal Dimension Characteristics[J]. Journal of Electronics & Information Technology, 2016, 38(4): 863-869. doi: 10.11999/JEIT150703

基于多重分形特征的通信調(diào)制方式識別研究

doi: 10.11999/JEIT150703
  • 1.

    (解放軍電子工程學(xué)院 合肥 230037) ②(解放軍95915部隊 天津 301612)

基金項目: 

國家自然科學(xué)基金(61571446)，安徽省自然科學(xué)基金(KY13C152)

Communication Modulation Recognition Based on Multi-fractal Dimension Characteristics

  • 1.

    (PLA Electronic Engineering Institute, Hefei 230037, China)

  • 2.

    (Unit 95915 of PLA, Tianjin 301612, China)

Funds: 

The National Natural Science Foundation of China (61571446), The Natural Science Foundation of Anhui Province (KY13C152)

  • 摘要: 該文提出一種基于多重分形特征的數(shù)字通信信號調(diào)制方式識別新方法。對接收信號進(jìn)行去噪預(yù)處理，基于2維數(shù)據(jù)陣列求取信號的廣義維數(shù)和多重分形譜；詳細(xì)討論了權(quán)重因子q對多重分形譜的影響，提取了13個多重分形特征參數(shù)；設(shè)計了基于多重分形特征的支持向量機(jī)分類器對不同信號進(jìn)行調(diào)制方式識別。仿真結(jié)果表明，該算法在低信噪比情況下具有很好的識別性能。
    Abstract: A new modulation recognition algorithm of digital communication signals based on the multi-fractal dimension characteristics is proposed. Employing preprocessing the received signal, the generalized dimension and the multi-fractal spectrum can be calculated by 2D data array. The effect of multi-fractal spectrum due to the weighted factorsq is discussed in detail, the 13 multi-fractal characteristic parameters are extracted. The Support Vector Machine (SVM) classifier based on the multi-fractal dimension characteristics is designed for recognition of different modulation signals. Simulation results show that the proposed method has a good recognition performance under low SNR.
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出版歷程
  • 收稿日期:  2015-06-08
  • 修回日期:  2015-12-11
  • 刊出日期:  2016-04-19

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