Normalization of Statistical Properties of Sea Clutter Based on Non-coherent Accumulation

  • Yi LiuEmail author
  • Shufang Zhang
  • Jidong Suo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


For clutter with long tailing characteristics, such as Weibull distribution, Ruili distribution, lognormality, and K-distribution, CFAR processors corresponding to various distributions need to be used when CFAR processing technology is used. Otherwise, it is difficult to obtain constant false alarm characteristics when the statistical characteristics of clutter change. From the viewpoint of improving the robustness of CFAR, according to the central limit theorem and the logarithmic compression principle of the signal, this paper attempts to accumulate and average the radar signal before CFAR processing according to the pulse accumulation characteristics and median limit theorem of radar signals. The clutter of the post-PDF is close to a normal distribution, which effectively eliminates the trailing effect of the clutter characteristics, i.e., it effectively suppresses the sharp peak interference and the distribution characteristics of the normalized clutter.


CFAR Radar pulse accumulation Normalization Smearing Radar clutter 


  1. 1.
    Raghavan RS. CFAR detection in clutter with a Kronecker covariance structure. IEEE Trans Aerosp Electron Syst. 2017;53(2):619–29.CrossRefGoogle Scholar
  2. 2.
    Finn HM, Johnson RS. Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates. RCA Rev. 1968;29:414–64.Google Scholar
  3. 3.
    Youzhong H, Yingning P et al. Radar automatic detection and processing. Beijing: Tsinghua University Press; 1999.Google Scholar
  4. 4.
    Chengpeng H, Chaohuan H, Jin R, Jianping Y. Performance analysis of OSGO- and PSSO-CFAR in K clutter background. J Electron Inf Technol. 2005;27(7):1061–4.Google Scholar
  5. 5.
    Chengpeng H, Chaohuan H, Jianping Y. Performance analysis of OSCA-CFAR in K clutter background. Electron Inf Countermeas Technol. 2006;21(2).Google Scholar
  6. 6.
    Abdel-Nabi MA, Seddik KG, El-Badawy E-A. Spiky Sea clutter and constant false alarm rate processing in high-resolution maritime radar systems. In: International conference on computer and communication engineering (ICCCE 2012), 3–5 July 2012, Kuala Lumpur, Malaysia.Google Scholar
  7. 7.
    Yamauchi H, Somha W.A noise suppressing filter design for reducing deconvolution error of both-directions downward sloped asymmeric RTN long-tail distributions. In: 2015 International workshop on CMOS variability (VARI); 2015. p. 51–6.Google Scholar
  8. 8.
    Shi Z, Zhou J, Zhao H, Fu Q. Robust estimation of scattering center parameters in long-tailed K-distribution clutter. J Electron Inf Technol. 2007;29(12).Google Scholar
  9. 9.
    Ritcey JA. Radar Detection in K-distributed clutter plus noise using L-statistics. 978-1-5386-1823-3/17/$31.00 ©2017 IEEEGoogle Scholar
  10. 10.
    Yi Z, Xiubin Z, Xiaohua T, Hongsen X. Knowledge-aided signal detection algorithm in non-Gaussian clutter. Sig Process. 2012;28(1):60–6.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Information Science and Technology, Dalian Maritime UniversityDalianChina

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