Study on fractal features of modulation signals

被引:3
|
作者
Tiejun Lü
Shuangbing Guo
Xianci Xiao
机构
[1] Tsinghua University,Department of Automatization
[2] University of Electronic Science and Technology of China,Department of Electronic Engineering
来源
关键词
modulation recognition; feature extracting; fractal; noise interfere;
D O I
10.1007/BF02713973
中图分类号
学科分类号
摘要
Based on fractal theory, the note presents a novel method of modulation signals classification that adopts box dimension and information dimension extracted from received signals as features of classification. These features contain the characteristics of magnitude, frequency and phase of signals, and collect discriminatory information among various modulation modes. They are effective features in classification sense, and are insensitive to noises interfering. The theoretical analysis also proves the above conclusion. The classifier design is very simple based on such features. The simulation results show that the performances of signal classification are superior.
引用
收藏
页码:152 / 158
页数:6
相关论文
共 50 条
  • [1] Study on fractal features of modulation signals
    吕铁军
    郭双冰
    肖先赐
    [J]. Science China(Information Sciences), 2001, (02) : 152 - 158
  • [2] Circadian Rhythms in Fractal Features of EEG Signals
    Croce, Pierpaolo
    Quercia, Angelica
    Costa, Sergio
    Zappasodi, Filippo
    [J]. FRONTIERS IN PHYSIOLOGY, 2018, 9
  • [3] Verification and Recognition of Fractal Characteristics of Communication Modulation Signals
    Li, Jingchao
    Ying, Yulong
    Lin, Yun
    [J]. PROCEEDINGS OF 2019 IEEE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION AND COMMUNICATION TECHNOLOGY (ICEICT 2019), 2019, : 304 - 309
  • [4] Multi-scale fractal analysis of modulation signals
    Li Bing
    Chen Shuang-shuang
    Dong Jun
    Liu Peng-yuan
    [J]. SIXTH INTERNATIONAL CONFERENCE ON ELECTRONICS AND INFORMATION ENGINEERING, 2015, 9794
  • [5] Fractal and multi-fractal features of the broadband power line communication signals
    Ma, Yuan-Jia
    Zhai, Ming-Yue
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2018, 72 : 566 - 576
  • [6] COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES
    Wang, Yu
    Zhou, Weidong
    Yuan, Qi
    Li, Xueli
    Meng, Qingfang
    Zhao, Xiuhe
    Wang, Jiwen
    [J]. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2013, 23 (06)
  • [7] Analysis and classification of speech signals by generalized fractal dimension features
    Pitsikalis, Vassilis
    Maragos, Petros
    [J]. SPEECH COMMUNICATION, 2009, 51 (12) : 1206 - 1223
  • [8] Combining spectral and fractal features for emotion recognition on Electroencephalographic signals
    [J]. 1600, World Scientific and Engineering Academy and Society, Ag. Ioannou Theologou 17-23, Zographou, Athens, 15773, Greece (10):
  • [9] Extracting self-affine (fractal) features from physiologic signals
    Michieli, I.
    Rogina, B. Medved
    [J]. 2007 14TH INTERNATIONAL WORKSHOP ON SYSTEMS, SIGNALS, & IMAGE PROCESSING & EURASIP CONFERENCE FOCUSED ON SPEECH & IMAGE PROCESSING, MULTIMEDIA COMMUNICATIONS & SERVICES, 2007, : 25 - 28
  • [10] Fractal analyses of HRV signals: A comparative study
    Akay, M
    Fischer, R
    [J]. METHODS OF INFORMATION IN MEDICINE, 1997, 36 (4-5) : 271 - 273