Compound Gaussian Radar Clutter Model With Positive Tempered Alpha-Stable Texture

TL;DR

This paper introduces a flexible compound Gaussian radar clutter model using the positive tempered alpha-stable distribution to better capture the diverse tail behaviors observed in sea clutter data.

Contribution

It develops a novel bivariate isotropic clutter model with explicit characteristic function and a parameter estimation method based on characteristic functions.

Findings

Model effectively captures various tail behaviors in real sea clutter data

Amplitude model expressed as a scale mixture of Rayleigh distributions

Enhanced flexibility over existing clutter models

Abstract

The compound Gaussian (CG) family of distributions has achieved great success in modeling sea clutter. This work develops a flexible-tailed CG model to improve generality in clutter modeling, by introducing the positive tempered $\alpha$-stable (PT$\alpha$S) distribution to model clutter texture. The PT$\alpha$S distribution exhibits widely tunable tails by tempering the heavy tails of the positive $\alpha$-stable (P$\alpha$S) distribution, thus providing greater flexibility in texture modeling. Specifically, we first develop a bivariate isotropic CG-PT$\alpha$S complex clutter model that is defined by an explicit characteristic function, based on which the corresponding amplitude model is derived. Then, we prove that the amplitude model can be expressed as a scale mixture of Rayleighs, just as the successful compound K and Pareto models. Furthermore, a characteristic function-based method is developed to estimate the parameters of the amplitude model. Finally, real-world sea clutter data analysis indicates the amplitude model's flexibility in modeling clutter data with various tail behaviors.