  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/.
