Parameter Estimation and Modeling Performance of Compound-Gaussian Clutter with Weibull-Distributed Texture

In plain language

Accurate modelling of radar sea clutter is vital for target detection, particularly in challenging environments. The compound-Gaussian clutter model with Weibull-distributed texture serves as an effective statistical tool to describe low-resolution sea clutter observed at low grazing angles. This two-parameter distribution relies on precise estimates of its shape and scale parameters to represent clutter environments accurately. To improve this estimation, a novel fractional negative order moment estimator is introduced. The approach is evaluated against established estimation techniques, including the standard method of moments, the method of fractional-order moments, and a logarithmic method. Assessment of the new estimator using statistical criteria, specifically the Kolmogorov-Smirnov test and mean square error metrics, establishes a rigorous baseline for its parameter estimation accuracy in marine radar clutter contexts.

Key takeaways

The compound-Gaussian model with Weibull-distributed texture captures low-resolution sea clutter at low grazing angles.
A fractional negative order moment estimator is introduced to calculate the distribution's shape and scale parameters.
Estimator accuracy is assessed using both Kolmogorov-Smirnov and mean square error metrics.
The proposed method is benchmarked against the method of moments, fractional-order moments, and a logarithmic-based estimation technique.

Why it matters

Radar systems operating over oceans frequently struggle to differentiate real targets from background sea interference, known as clutter. By improving the mathematical estimation of sea clutter parameters, engineering teams can better understand radar noise in marine conditions. This forms a foundational step towards improving radar signal processing and detection reliability.

Commercialisation angle

The research is relevant to developers of marine radar systems and signal processing algorithms who require accurate sea clutter modelling. Because the work focuses purely on mathematical estimation and statistical validation against existing techniques, it represents early-stage theoretical research with no direct commercial prototype or deployed tool described in the abstract.

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Abstract

This paper addresses the parameter estimation of the compound-Gaussian clutter with Weibull distributed texture (CGWB). The CGWB distribution is introduced to model sea clutter, and it is adequate for low-resolution sea clutter at low grazing angles. The CGWB distribution is one of the biparametric distributions characterized by two parameters: the shape parameter and scale parameter. In this work, the fractional negative order moment estimator (FNOME) is proposed for CGWB parameter estimation. The accuracy of the FNOME is evaluated using both the Kolmogorov-Smirnov (KS) and the mean square error (MSE) criteria. The FNOME performance is compared to existing estimators: method of moments (MoM), method of fractional-order moments (MoFM), and [zlog(z)]-based method.

Research topics

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