  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.
