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Radar Clutter Modeling Based on CGIG and Mixture CGIG Distributions | IEEE Conference Publication | IEEE Xplore

Radar Clutter Modeling Based on CGIG and Mixture CGIG Distributions


Abstract:

Development of target CFAR (Constant False Alarm Rate) detection schemes requires the specification of radar clutter models. There is why the modeling study of non-statio...Show More

Abstract:

Development of target CFAR (Constant False Alarm Rate) detection schemes requires the specification of radar clutter models. There is why the modeling study of non-stationary radar sea-clutter statistics is a first serious research topic in favor of the above challenge. Several scenes of real data consist of mixture Gaussian and non-Gaussian statistics. To obtain an accurate fitting to IPIX (Intelligent Pixel X-band) sea clutter, compound Gaussian Inverse Gaussian (CGIG) class distribution is considered in this work against standard distributions labeled Weibull, log-normal, Pareto type II and K. Parameters values of the above models are estimated from IPIX data using MLE (Maximum Likelihood Estimation) method and LSA (Least Squares Approximation) method. In most cases of IPIX database, experimental study shows that CGIG and mixture CGIG CCDFs (Complementary Cumulative Distributed Function) can fit empirical CCDF in terms of range cell resolution, antennas polarization and range cell number.
Date of Conference: 21-23 October 2023
Date Added to IEEE Xplore: 01 November 2023
ISBN Information:
Conference Location: Giza, Egypt
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I. Introduction

Statistical modeling of clutter amplitude or intensity distributions is of great importance in radar signal processing search community [1]. For maritime surveillance radars, sea clutter of quite low resolution can be modeled for instance by the exponential distribution in the intensity domain. However, this model is restricted by some sea surface conditions and radar system parameters. Nevertheless, this model can not characterize reverberation data of higher spatial resolution at low grazing angles and high wind speed. For this reason, a variety of non-exponential probability density functions (pdf) are proposed to model radar sea clutter with heavy tails or ‘spikiness’, including gamma, log-normal, Weibull distributions and five types of compound-Gaussian (CG) distribution [2]. The latter is obtained by the combination of two different pdfs representing the speckle and the texture components. The speckle has an exponential distribution with a power parameter. The latter is called the texture component and is a random variable which can follow gamma, inverse gamma, inverse Gaussian or log-normal distribution [3]. In fact, each texture distribution has a particular representation of sea echoes for a high resolution case. Many non-coherent/coherent CFAR target detection schemes are constructed under these models [4].

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