Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at https://osf.io/mnsgk/.

翻译：无人机的应用日益广泛且具有诸多生产性用途，但其普及也引发了安全关切——如何保护受限空域？识别无人机类型有助于评估其潜在风险，例如固定翼飞行器可携带更重载荷飞越更远距离，因此可能构成更严重威胁。本文提出一种机器学习模型，用于将无人机分类为四旋翼、六旋翼或固定翼三类。我们的方法有效应用长短期记忆神经网络进行时间序列分类。通过实验探究时间戳采样方法变更与类别分布不平衡处理的影响，我们确定了性能最优的采样方法与类别不平衡修正策略。经10折数据宏F1分数平均计算，多数类四旋翼预测准确率高达98.16%，尽管存在极端类别不平衡，模型对多数固定翼飞行轨迹的预测正确率仍达73.15%。六旋翼样本常因多旋翼飞行器的普遍相似性被误判为四旋翼（42.15%）。但特定方法下的结果保持相对稳定，这促使我们分析并报告其权衡关系。本文补充材料包含运行全部实验与生成结果表格的代码及数据，可通过https://osf.io/mnsgk/获取。