# Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification

arXiv: https://arxiv.org/abs/2403.00565 · HTML: https://mlsanity.com/paper/2403.00565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition](https://mlsanity.com/paper/1512.06747/) (2015)
- [The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models](https://mlsanity.com/paper/2005.10111/) (2020)
- [ALFA: A Dataset for UAV Fault and Anomaly Detection](https://mlsanity.com/paper/1907.06268/) (2019)
- [Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks](https://mlsanity.com/paper/1701.05779/) (2017)

## Abstract (arXiv)

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/.