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Automatic target recognition of time critical moving targets using1D high range resolution (HRR) radar

机译:使用一维高分辨力(HRR)雷达自动识别时间关键的移动目标

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Synthetic Aperture Radar (SAR) imaging and Automatic Target Recognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has recently received increased interest. High Range Resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T/(C+N)) via Doppler filtering and/or clutter cancellation. A goal of HRR ATR transition is the implementation and evaluation of algorithms exhibiting robustness under extended operating conditions (EOC). The public domain Moving and Stationary Target Acquisition and Recognition (MSTAR) data set was used to study 1D template-based ATR development and performance. Due to the unavailability of a statistically significant moving ground target data set, this approach was taken as an interim step in assessing the separability of ground targets when using range only discriminants. This report summarizes the data and algorithm methodology, simulated performance results, and recommendations
机译:由于聚焦移动目标固有的困难,移动目标的合成孔径雷达(SAR)成像和自动目标识别(ATR)构成了重大挑战。结果,移动目标的ATR最近受到了越来越多的关注。高范围分辨率(HRR)雷达模式通过多普勒滤波和/或杂波消除技术形成聚焦的HRR轮廓,从而显着增强了目标到(杂波+噪声)(T /(C + N))的位置,从而提供了一种识别运动目标的方法。 HRR ATR过渡的目标是实现和评估在扩展操作条件(EOC)下表现出鲁棒性的算法。公共领域的移动和固定目标获取与识别(MSTAR)数据集用于研究基于1D模板的ATR开发和性能。由于缺乏具有统计意义的移动地面目标数据集,因此,当仅使用范围判别器时,此方法被用作评估地面目标可分离性的过渡步骤。本报告总结了数据和算法方法,模拟性能结果以及建议

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