# Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks

arXiv: https://arxiv.org/abs/2605.26310 · HTML: https://mlsanity.com/paper/2605.26310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 5 of 5)

- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis](https://mlsanity.com/paper/1911.07925/) (2019)
- [Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends](https://mlsanity.com/paper/2008.12461/) (2020)
- [VPNet: Variable Projection Networks](https://mlsanity.com/paper/2006.15590/) (2020)
- [Rational Gaussian wavelets and corresponding model driven neural networks](https://mlsanity.com/paper/2502.01282/) (2025)

## Abstract (arXiv)

The detection of unmanned aerial vehicles (UAVs) is important for the protection of civilian and military infrastructure. In this paper we propose a cost effective UAV detection system using sound signals obtained from microphones. The recorded signals are passed through a signal processing pipeline which employs interpretable adaptive feature extractors using so-called rational Gaussian wavelets. These adaptive wavelet transformations are embedded into and trained together with an underlying small neural network which detects and classifies UAVs based on the obtained features. This leads to a physically interpretable machine learning algorithm that in addition to classifying UAVs is also capable of detecting UAV swarms. We demonstrate our results using data collected in indoor studio and noisy outdoor environments. We conclude that the proposed method outperforms traditional machine learning approaches for detecting and classifying single UAVs as well as drone swarms, while retaining a high degree of interpretability. Our implementation of the proposed methods is made publicly available for reproducibility.