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

Constant false alarm rate (CFAR) detectors are developed to track changes in clutter intensities and to adapt the detection threshold to maintain a constant probability of false alarms. These adaptive thresholding mechanisms are initially intended when both target and clutter are exponentially distributed, and they degrade in performance when applied to newer target and clutter models. So, in application scenarios like Airborne Warning and Control Systems (AWACS) and ship remote sensing, when both the target and clutter are Pareto distributed, instead of the conventional way of tweaking the existing adaptive-thresholding CFAR detector, we pose the detection problem as a two-sample, Pareto vs. Pareto composite hypothesis testing problem. 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 and provide extensive simulation results to demonstrate the performance of the proposed detector.