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arXiv · 2608.14287

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

Vadym Vilhurin·Volodymyr Sydorskyi·Andrii Shevtsov
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Abstract

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.

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Vadym Vilhurin, Volodymyr Sydorskyi, Andrii Shevtsov. 2026-08-14. Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels. https://arxiv.org/abs/2608.14287

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