• DocumentCode
    406218
  • Title

    A new time-frequency analysis based upon AR model

  • Author

    Wang Haijun ; Guizhong, Liu ; Fan Wanchun

  • Author_Institution
    Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., China
  • Volume
    1
  • fYear
    2003
  • fDate
    14-17 Dec. 2003
  • Firstpage
    648
  • Abstract
    In this paper we analyzed the reasons why the discrete Wigner-Ville-distribution (WVD) of real-valued signal sampled at the Nyquist rate has spectral aliasing, whereas short time Fourier transform (STFT) has not such problems. For the time-frequency resolution of STFT spectrogram is very poor, a novel method of time-frequency analysis based on auto-regressive model (AR) is presented in this paper, which inherits merits of STFT spectrogram and has very good time-frequency resolution. When data for processing are very large, the new method may have excellent performance for promoting velocity of calculating, saving storage and keeping high time-frequency resolution. In addition, the applications of the new method were also illustrated for identifying ripple-fired explosions, the results were compared with that of spectrogram. Experiments showed that the performances of the new algorithm were superior than that of spectrogram.
  • Keywords
    Fourier transform spectroscopy; Wigner distribution; autoregressive processes; signal resolution; signal sampling; time-frequency analysis; AR model; Fourier transform spectrogram; Nyquist rate; autoregressive model; discrete Wigner-Ville-distribution; short time Fourier transform; time-frequency analysis; Energy resolution; Explosions; Fourier transforms; Information analysis; Signal analysis; Signal generators; Signal processing; Signal resolution; Spectrogram; Time frequency analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    0-7803-7702-8
  • Type

    conf

  • DOI
    10.1109/ICNNSP.2003.1279358
  • Filename
    1279358