• DocumentCode
    1948288
  • Title

    An improved wavelet based shock wave detector

  • Author

    Wei Xie ; Ming Bao ; Xiaodong Li ; Xiao-Ping Zhang

  • Author_Institution
    Inst. of Acoust., Beijing, China
  • fYear
    2015
  • fDate
    12-15 July 2015
  • Firstpage
    55
  • Lastpage
    59
  • Abstract
    In this paper, the detection of shock wave that generated by supersonic bullet is considered. A wavelet based multi-scale products method has been widely used for detection. However, the performance of method decreased at low signal-to-noise ratio (SNR). It is noted that the method does not consider the distribution of the signal and noise. Thus we analyze the method under the standard likelihood ratio test in this paper. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An improved detector under the standard likelihood ratio test is proposed. Monte Carlo simulations is conducted with simulated shock waves under additive white Gaussian noise. The result shows that this new detection algorithm outperforms the conventional detection algorithm.
  • Keywords
    AWGN; Monte Carlo methods; mechanical engineering computing; shock waves; signal detection; statistical testing; wavelet transforms; weapons; Monte Carlo simulation; SNR; additive white Gaussian noise; likelihood ratio test; signal-to-noise ratio; supersonic bullet; wavelet based multiscale products method; wavelet based shock wave detector; Acoustics; Detectors; Image edge detection; Mathematical model; Shock waves; Signal to noise ratio; detector; edge detection; likelihood ratio test; shock wave; wavelet transform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on
  • Conference_Location
    Chengdu
  • Type

    conf

  • DOI
    10.1109/ChinaSIP.2015.7230361
  • Filename
    7230361