Effective Ground-Truthing of Supervised Machine Learning for Drone Classification
Effective Ground-Truthing of Supervised Machine Learning for Drone Classification
复制标题
用于无人机分类的监督机器学习的有效地面实况
DOI：
10.1109/radar41533.2019.171322
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
H. Dale
中科院分区：
文献类型：
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作者：
Jacob Sim;M. Jahangir;F. Fioranelli;C. Baker;H. Dale
已经表明，多波束凝视雷达能够检测和跟踪低可观测目标，如无人机，由于其高灵敏度[1]。由于这种灵敏度，与无人机具有类似RCS的目标也被检测和跟踪。主要是鸟类。鸟类和无人机在飞行高度、速度和机动性等几个方面都很相似[2]，因此很难区分它们。因此，需要寻找高性能的分类方法，例如机器学习。机器学习分类器的监督训练需要准确标记的训练数据。对于控制目标，例如无人机，来自机载GPS记录的真实数据可用于数据标记。然而，合适的鸟目标需要一个单独的数据收集方法，使关联的雷达输出的分类器进行有效的训练。本文展示了一种在GoogleEarth上收集和显示小目标的地面实况的方法，以便雷达数据可以适当地用于为机器学习、无人机和鸟类分类器创建准确的训练数据。分类性能的结果显示出高性能，是由更有效的真理数据的可用性的帮助。
It has already been shown that multibeam staring radar is able to detect and track low observable targets such as drones due to its high sensitivity [1]. Due to this level of sensitivity, targets that have a similar RCS to drones are also detected and tracked. These are predominantly birds. Birds and drones are similar in several ways such as flight altitude, velocity and manoeuvrability [2] such that discrimination between them is challenging. Hence, there is a need to look for high performing methods of classification, for example, machine learning. Supervised training of machine learning classifiers requires accurately labelled training data. For control targets, such as drones, truth data from the on-board GPS logging can be used for data labelling. However, opportune bird targets require a separate data collection method that enables association with the radar output for a classifier to be effectively trained. This paper shows a method of collecting and displaying ground-truth for small targets onto GoogleEarth so that the radar data can be appropriately used to create accurate training data for a machine learning, drone and bird classifier. Results of classification performance are presented showing high performance that is aided by the availability of more effective truth data.