Robust Drone Classification Using Two-Stage Decision Trees and Results from SESAR SAFIR Trials
Robust Drone Classification Using Two-Stage Decision Trees and Results from SESAR SAFIR Trials
复制标题
使用两阶段决策树和 SESAR SAFIR 试验的结果进行稳健的无人机分类
DOI：
10.1109/radar42522.2020.9114870
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Baker
中科院分区：
文献类型：
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作者：
M. Jahangir;B. I. Ahmad;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扩展的雷达能够探测到几公里范围内的小型无人机。然而，在这种非合作监视系统中，目标识别是必要的，因为对低RCS目标（如鸟类和地面物体（如行人和车辆））的检测灵敏度增加，减轻了错误目标报告的问题。为了区分无人机和其他混淆目标，提出了一种简单的两阶段监督学习方法。这种方法基于决策树分类器，并被证明在过滤掉非无人机目标方面是有效的。SESAR SAFIR试验在现实城市环境中测试初始u空间服务的现场试验表明，两阶段决策树分类器提供了鲁棒性判别，并具有最小的误报。
Non-cooperative surveillance of drones is an important consideration in the EU SESAR vision for the provision of U-space services. The Aveillant Gamekeeper multiple beam staring radar utilises extended dwells to be able to detect small drones at ranges of several kilometres. However, target discrimination is necessary with such non-cooperative surveillance system as the increased detection sensitivity against low RCS targets, such as birds and surface objects (e.g., pedestrians and vehicles), extenuates the problem of false target reports. A simple two-stage supervised learning approach is proposed in order to discriminate drones from other confuser targets. This approach is based on a decision tree classifier and is shown to be effective at filtering out non-drone, targets. Field trials from the SESAR SAFIR trials to test initial U-space services in realistic urban environments shows that the two-stage decision tree classifier provides robust discrimination with minimal false positives.