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Extended fractal feature for first-stage SAR target detection

机译:扩展的分形特征可用于第一阶段SAR目标检测

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Abstract: The Extended Fractal (EF) feature has been shown to lower the false alarm rate for the focus of attention (FOA) stage of a synthetic aperture radar (SAR) automatic target recognition (ATR) system. The feature is both contrast and size sensitive, and thus, can discriminate between targets and many types of cultural clutter at the earliest stages of the ATR. In this paper we modify the EF feature so that one can 'tune' the size sensitivity to the specific targets of interest. We show how to optimize the EF feature using target chip data from the public MSTAR database. We demonstrate improvements in performance for FOA algorithms that include the new feature by comparing the receiver operating characteristic (ROC) curves for all possible combinations of FOA algorithms incorporating EF, two-parameter CFAR, and variance features. Finally, we perform timing experiments on the fused detector to demonstrate the feasibility for implementation of the detector in a real system. !14
机译:摘要:扩展分形(EF)功能已显示出可降低合成孔径雷达(SAR)自动目标识别(ATR)系统的关注焦点(FOA)阶段的误报率。该功能对对比度和大小都很敏感,因此可以在ATR的最早阶段区分目标和许多类型的文化杂物。在本文中,我们修改了EF功能,以便人们可以“调整”尺寸灵敏度以适应特定的目标。我们展示了如何使用公共MSTAR数据库中的目标芯片数据来优化EF功能。我们通过比较包含EF,两参数CFAR和方差特征的FOA算法的所有可能组合的接收器工作特性(ROC)曲线,论证了包括新功能的FOA算法的性能改进。最后,我们在融合探测器上进行时序实验,以证明在实际系统中实现探测器的可行性。 !14

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