• DocumentCode
    1882461
  • Title

    Radar target recognition method based on physical-statistical model

  • Author

    Feng Dejun ; Xu Letao ; Dahai, Dai

  • Author_Institution
    Sch. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2012
  • fDate
    12-15 Aug. 2012
  • Firstpage
    5
  • Lastpage
    10
  • Abstract
    A novel radar target recognition method based on physical-statistical model is proposed, which uses sequences of full-polarization high resolution radar range-profiles (HRRP). The HRRP of target is modeled as a Middleton´s Class A non-Gaussian distribution through a physical consideration. It is found that the model parameters describe the scattering physical characteristics of radar target and can be chosen as feature vector for radar target recognition. The Parzen window is adopted to estimate the probability distribution function of model parameter and the classifier is designed via Bayes theorem. The approach is applied to the classification of measured data. Numerical results have shown that the approach can extract the features which are not sensitive to target orientation and are effective to radar target recognition.
  • Keywords
    Bayes methods; Gaussian distribution; radar target recognition; statistical analysis; Bayes theorem; Middleton´s Class A non-Gaussian distribution; Parzen window; full-polarization high resolution radar range-profiles sequences; physical-statistical model; probability distribution function; radar target recognition; Feature extraction; Noise; Probability density function; Radar; Scattering; Target recognition; Vectors; Full-polarization; High resolution range profiles; Physical-Statistical model; Radar target recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing, Communication and Computing (ICSPCC), 2012 IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4673-2192-1
  • Type

    conf

  • DOI
    10.1109/ICSPCC.2012.6335648
  • Filename
    6335648