• DocumentCode
    2799713
  • Title

    A new method of radar clutter recognition based on multi-α truncation set

  • Author

    Xueli, Fang ; Diannong, Liang ; Kun, Zou ; Zhen, Dong ; Xiaotao, Huang

  • Author_Institution
    Sch. of Electron. Sci. & Technol., Nat. Univ. of Defense Technol., Hunan, China
  • Volume
    2
  • fYear
    2003
  • fDate
    8-13 Oct. 2003
  • Firstpage
    858
  • Abstract
    Radar clutter recognition is very useful for modern radar signal processing system. α truncation set feature extraction based method needs short samples and has very high recognition rate for log-normal distribution clutter, but suffers shortcomings of high error recognition rate to discriminate Weibull from K-distribution when the samples are short. A modified radar clutter recognition method based on α truncation set feature extraction - multi-α truncation set feature extraction based radar clutter recognition method is presented in this paper, we point out that this method is a generalized form of rectangular figure-based method. From the simulation results and analyzing of real radar clutter test, it is seen that this method is more valid, practical and prospective.
  • Keywords
    Weibull distribution; feature extraction; log normal distribution; radar clutter; radar signal processing; Weibull distribution; error recognition rate; feature extraction; log-normal distribution clutter; multi-α truncation set; radar clutter recognition; radar clutter test; radar signal processing system; rectangular figure based method; Analytical models; Feature extraction; Higher order statistics; Log-normal distribution; Parametric statistics; Radar clutter; Radar signal processing; Signal processing; Statistical analysis; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
  • Print_ISBN
    0-7803-7925-X
  • Type

    conf

  • DOI
    10.1109/RISSP.2003.1285699
  • Filename
    1285699