CLASS U-space drone test flight results for non-cooperative surveillance using an L-band 3-D staring radar
CLASS U-space drone test flight results for non-cooperative surveillance using an L-band 3-D staring radar
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使用 L 波段 3-D 凝视雷达进行非合作监视的 CLASS U-space 无人机试飞结果
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
C. Baker
中科院分区：
文献类型：
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作者：
M. Jahangir;C. Baker
无人机的非合作监视是欧盟SESAR提供U空间服务愿景中的一个重要考虑因素。Aveillant GameKeeper多波束凝视雷达利用延长的驻留时间，能够探测到几公里范围内的小型无人机。然而，目标的歧视是必要的，这样的监视系统作为增加对低RCS目标的检测灵敏度减轻了虚假报告的问题，如鸟类和车辆等表面物体的目标，机器学习分类器被用来删除混淆目标，如鸟类，以提供实时跟踪的无人机。SESAR CLASS项目现场无人机飞行对U空间的几个测试场景的现场试验被用来训练和测试一个决策树分类器，该分类器在轨迹和微多普勒特征上工作。结果表明，一个高水平的分类器的准确性是在一个旋翼无人机的飞行剖面范围内实现的。
Non-cooperative surveillance of drones is an important consideration in the EU SESAR vision for the provision of U-space services. Aveillant Gamekeeper multiple beam staring radar utilises extended dwell to be able to detect small drones at the range of several kilometres. However, target discrimination is necessary with such surveillance system as the increased detection sensitivity against low RCS targets extenuates the problem of false reports of targets such as birds and surface objects such as vehicles etc. Machine learning classifiers are used to remove confuser targets such as birds to provide real-time tracks of drones. Field trials from SESAR CLASS project live drone flights against several test scenarios for U-space are used to train and test a decision tree classifier working on both trajectory and micro-doppler features. Results show that a high level of classifier accuracy is achieved across a range of flight profiles for a rotary wing drone.