Sea clutter statistics based on similarity fitting of classical models

Zhao Di; Meng Junmin; Zhang Xi; Lang Haitao

doi:10.3969/j.issn.0253-4193.2015.05.011

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Zhao Di, Meng Junmin, Zhang Xi, Lang Haitao. Sea clutter statistics based on similarity fitting of classical models[J]. Haiyang Xuebao, 2015, 37(5): 112-120. doi: 10.3969/j.issn.0253-4193.2015.05.011

Citation:

Zhao Di, Meng Junmin, Zhang Xi, Lang Haitao. Sea clutter statistics based on similarity fitting of classical models[J]. Haiyang Xuebao, 2015, 37(5): 112-120. doi: 10.3969/j.issn.0253-4193.2015.05.011

Zhao Di, Meng Junmin, Zhang Xi, Lang Haitao. Sea clutter statistics based on similarity fitting of classical models[J]. Haiyang Xuebao, 2015, 37(5): 112-120. doi: 10.3969/j.issn.0253-4193.2015.05.011

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Sea clutter statistics based on similarity fitting of classical models

doi: 10.3969/j.issn.0253-4193.2015.05.011

1.
Department of Applied Physics, Beijing University of Chemical Technology, Beijing 100029, China
2.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
3.
Department of Applied Physics, Beijing University of Chemical Technology, Beijing 100029, China;Engineering Research Center for Marine Telemetry, State Oceanic Administration & China National Space Administration, Qingdao 266061, China

Received Date: 2014-02-21
Rev Recd Date: 2014-06-16

Abstract

Abstract

A sea clutter statistical method based on similarity fitting is proposed in this paper. We first estimate five classical probability density functions of the sea clutter distribution in synthetic aperture radar (SAR) imagery,which include Rayleigh distribution,lognormal distribution,Weibull distribution,K distribution and G⁰ distribution. Thereafter,we fit all these five models to a new sea clutter distribution by an optimization method based on a similarity criterion. In the experiment,we evaluate the fitting precision based on the Kullback-Leibler distance using four SAR images. The results show the K-L distance between the fitting model and observations is only 0.015 84 when using the proposed algorithm. The fitting model is concluded to be superior to five classical distribution models. When we conduct ship detection using the fitting model and CFAR algorithm,the mean detection accuracy can reach up to 95.75%.
- sea clutter statistical model,
- constant false alarm rate (CFAR),
- ship detection,synthetic aperture radar (SAR),

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References(18)

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