GLRT based CFAR Pareto-Target Aircraft Detection in Two-Parameter Pareto Distributed Clutter
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In the last decade, after Pareto distribution has been validated for X-band high-resolution maritime clutter returns, new detection schemes were designed, and heuristics for constant false alarm rate (CFAR) processors appeared in the literature. These schemes used the same form of adaptive thresholding that was originally derived for detecting Swerling-I target in exponentially distributed clutter. Such an approach to get a CFAR would affect the detection performance when applied to different target and clutter models. Very recently, 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 the context of Pareto Clutter, we pose a Pareto distributed target-fluctuating-model or Pareto-Target (PT) aircraft detection problem as a two-sample, Pareto vs. Pareto composite hypothesis testing problem. We solve this problem systematically from the first principles of Neyman Pearson (NP)- lemma first to simple vs. composite, and then for a more realistic composite vs. composite while considering no knowledge of both scale and shape parameters of Pareto distributed clutter. For the composite case, we derive the generalized likelihood ratio test (GLRT) statistic and show that the GLRT test statistic is a constant false alarm rate (CFAR) detector. We provide extensive simulation results to demonstrate the performance of the proposed detectors.
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