GLRT based Adaptive-Thresholding for CFAR-Detection of Pareto-Target in Pareto-Distributed Clutter

TL;DR

This paper develops a GLRT-based adaptive thresholding method for CFAR detection of Pareto-distributed targets in Pareto clutter, addressing the limitations of traditional methods designed for exponential models, and demonstrating its effectiveness through simulations.

Contribution

It introduces a novel GLRT-based adaptive thresholding detector for Pareto-Pareto hypothesis testing in radar detection, which maintains CFAR properties without requiring parameter knowledge.

Findings

The proposed detector exhibits CFAR property in Pareto scenarios.

Simulation results show improved detection performance over traditional methods.

The method effectively handles unknown scale and shape parameters in Pareto clutter.

Abstract

After Pareto distribution has been validated for sea clutter returns in varied scenarios, some heuristics of adaptive-thresholding appeared in the literature for constant false alarm rate (CFAR) criteria. These schemes used the same adaptive-thresholding form that was originally derived for detecting Swerling-I (exponential) target in exponentially distributed clutter. Statistical procedures obtained under such idealistic assumptions would affect the detection performance when applied to newer target and clutter models, esp. heavy tail distributions like Pareto. Further, in addition to the sea clutter returns, it has also been reported that Generalized Pareto distribution fits best for the measured Radar-cross-section (RCS) data of a SAAB aircraft. Therefore, in Radar application scenarios like Airborne Warning and Control System (AWACS), when both the target and clutter are Pareto distributed, we pose the detection problem as a two-sample, Pareto vs. Pareto composite hypothesis testing problem. We address this problem by corroborating the binary hypothesis framework instead of the conventional way of tweaking the existing adaptive-thresholding CFAR detector. Whereby, for the composite case, considering no knowledge of both scale and shape parameters of Pareto distributed clutter, we derive the new adaptive-thresholding detector based on the generalized likelihood ratio test (GLRT) statistic. We further show that our proposed adaptive-thresholding detector has a CFAR property. We provide extensive simulation results to demonstrate the performance of the proposed detector.