# Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels

arXiv: https://arxiv.org/abs/2608.14287 · HTML: https://mlsanity.com/paper/2608.14287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 19 of 19)

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- [Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization](https://mlsanity.com/paper/1912.04761/) (2019)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
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- [TRIDENT: Tri-modal Real-time Intrusion Detection Engine for New Targets](https://mlsanity.com/paper/2504.06417/) (2025)
- [Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference](https://mlsanity.com/paper/2003.01519/) (2020)
- [Adversarial Validation Approach to Concept Drift Problem in User Targeting Automation Systems at Uber](https://mlsanity.com/paper/2004.03045/) (2020)
- [Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices](https://mlsanity.com/paper/2110.09598/) (2021)

## Abstract (arXiv)

Passive acoustic sensing offers a critical, cost-efficient, and, crucially, passive alternative for detecting small unmanned aerial vehicles. However, the practical deployment of acoustic systems is discouraged by extreme environmental noise and sensor-induced domain shift caused by heterogeneous hardware. This paper addresses these challenges by introducing a robust framework optimized for real-world battlefield conditions. We propose the integration of Per-Channel Energy Normalization (PCEN) and attention-based pooling to enhance feature extraction under low signal-to-noise ratio scenarios. We further propose a domain-aware training strategy that leverages auxiliary classes and multi-microphone data to mitigate cross-domain performance degradation. Evaluated on a unique dataset of combat-zone recordings from the Ukrainian frontlines, our approach significantly outperforms existing baselines, increasing the F1 score from 55.4% to 78.6%. This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026.