Abstract
Previous studies have shown that, for certain data sets, segmentation can help target detection performance for the Matched Filter (MF) algorithm. In this paper, we study the implementation of clustering prior to the Adaptive Cosine Estimator (ACE) calculation and compare our results to the classic non-segmented ACE and Matched Filter algorithms. From our results, we conclude that the proposed algorithm improves Matched Filter results in low false alarm rate conditions, achieving higher accuracy and lower false alarms in target detection; the ACE algorithm results are only marginally affected by segmentation.
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