• DocumentCode
    2385459
  • Title

    Radar HRRP target recognition in frequency domain based on autoregressive model

  • Author

    Wang, Penghui ; Dai, Fengzhou ; Pan, Mian ; Du, Lan ; Liu, Hongwei

  • Author_Institution
    Nat. Lab. of Radar Signal Process., Xidian Univ., Xi´´an, China
  • fYear
    2011
  • fDate
    23-27 May 2011
  • Firstpage
    714
  • Lastpage
    717
  • Abstract
    In this paper, we adopt the autoregressive (AR) model to characterize the frequency spectrum amplitude of high resolution range profile (HRRP) and extract the AR and partial correlation (PARCOR) coefficients, which are invariant to the initial-phase, translation and scale changes of HRRP, as discriminating features. Moreover, a mixture model based frame partition method is proposed and a Bayesian Ying-Yang (BYY) harmony learning algorithm is adopted to determine the frame number automatically during parameter learning. Experimental results based on measured data demonstrate the proposed features are superior to others in their minor frame number, robustness to sample size and good rejection ability.
  • Keywords
    Bayes methods; autoregressive processes; object detection; radar detection; Bayesian Ying-Yang harmony learning algorithm; autoregressive model; frequency domain; high resolution range profile; mixture model based frame partition method; parameter learning; partial correlation coefficients; radar HRRP target recognition; Bayesian methods; Feature extraction; Radar; Radar signal processing; Sensitivity; Target recognition; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Radar Conference (RADAR), 2011 IEEE
  • Conference_Location
    Kansas City, MO
  • ISSN
    1097-5659
  • Print_ISBN
    978-1-4244-8901-5
  • Type

    conf

  • DOI
    10.1109/RADAR.2011.5960631
  • Filename
    5960631