• DocumentCode
    59723
  • Title

    Radar High-Speed Target Detection Based on the Scaled Inverse Fourier Transform

  • Author

    Jibin Zheng ; Tao Su ; Wentao Zhu ; Xuehui He ; Qing Huo Liu

  • Author_Institution
    Nat. Lab. of Radar Signal Process., Xidian Univ., Xi´an, China
  • Volume
    8
  • Issue
    3
  • fYear
    2015
  • fDate
    Mar-15
  • Firstpage
    1108
  • Lastpage
    1119
  • Abstract
    In this paper, by employing the symmetric autocorrelation function and the scaled inverse Fourier transform (SCIFT), a coherent detection algorithm is proposed for high-speed targets. This coherent detection algorithm is simple and can be easily implemented by using complex multiplications, the fast Fourier transform (FFT) and the inverse FFT (IFFT). Compared to the Hough transform and the keystone transform, this coherent detection algorithm can detect high-speed targets without the brute-force searching of unknown motion parameters and achieve a good balance between the computational cost and the antinoise performance. Through simulations and analyses for synthetic models and the real data, we verify the effectiveness of the proposed coherent detection algorithm.
  • Keywords
    object detection; remote sensing by radar; Hough transform; antinoise performance; brute-force searching; coherent detection algorithm; computational cost; fast Fourier transform; keystone transform; motion parameters; radar high-speed target detection; scaled inverse Fourier transform; symmetric autocorrelation function; Algorithm design and analysis; Coherence; Computational efficiency; Object detection; Radar detection; Transforms; Coherent detection; fast Fourier transform (FFT); inverse fast Fourier transform (IFFT); scaled inverse Fourier transform (SCIFT); symmetric autocorrelation function;
  • fLanguage
    English
  • Journal_Title
    Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    1939-1404
  • Type

    jour

  • DOI
    10.1109/JSTARS.2014.2368174
  • Filename
    6967775