New Acoustic UAV Detection System Boosts F1 Score on Ukrainian Battlefield Data

Researchers have developed a new framework for detecting unmanned aerial vehicles (UAVs) using acoustic sensing in noisy battlefield environments. The system integrates Per-Channel Energy Normalization (PCEN) and attention-based pooling to improve feature extraction in low signal-to-noise conditions. A domain-aware training strategy was also implemented to address performance degradation across different sensor hardware. Tested on data from the Ukrainian frontlines, this approach significantly improved the F1 score from 55.4% to 78.6%. AI

IMPACT This research could enhance battlefield awareness and safety through improved passive detection of small aerial threats.

RANK_REASON The cluster contains an academic paper detailing a new technical approach and experimental results. [lever_c_demoted from research: ic=1 ai=0.7]

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• arXiv
• Bath
• information systems technology
• International Conference on Military Communication and Information Systems (ICMCIS)
• IST-224-RSY
• Ukrainian
• Volodymyr Sydorskyi

• paper
• other

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