• DocumentCode
    1797844
  • Title

    Individual radiation source identification based on fractal box dimension

  • Author

    Jingchao Li ; Yulong Ying

  • Author_Institution
    Coll. of Electron. Inf., Shanghai Dianji Univ., Shanghai, China
  • fYear
    2014
  • fDate
    15-17 Nov. 2014
  • Firstpage
    676
  • Lastpage
    681
  • Abstract
    Nowadays, it is difficult to identify the individual radiation source under low SNR environment. To this problem, the paper proposed a new fractal box dimension based algorithm, to calculate the fractal box dimension of different communication individual radio signals as the subtle characteristics. Basing on the traditional fractal box dimension, the proposed algorithm calculated the derivations of different reconstructing phase space points, and getting the fractal box dimension of the communications signals under different reconstructing conditions, which constitute a feature vector, to realize the purpose of extracting the subtle characteristics of individual radiation source more exactly. Finally, neural network was used to process and classify the fractal box dimension vector features, in order to achieve the purpose of classifying and recognizing different communication radiation source under complex environment.
  • Keywords
    feature extraction; identification; neural nets; radiocommunication; signal reconstruction; telecommunication computing; communication radiation source; communication signals; feature vector; fractal box dimension based algorithm; individual radiation source identification; low SNR environment; neural network; phase space point reconstruction; radio signals; Feature extraction; Fitting; Fractals; Signal to noise ratio; Space stations; Support vector machine classification; Vectors; Communication radio identification; Feature extraction; Fractal box dimension; Subtle features;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2014 2nd International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-5457-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2014.7009371
  • Filename
    7009371