战场场景中的无人机声学检测：处理噪声、域偏移与弱标签
Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels
- Institute for Applied System Analysis, Igor Sikorsky Kyiv Polytechnic Institute（伊戈尔·西科尔斯基基辅理工学院应用系统分析研究所）
- Zvook（兹沃克公司）
- Kyiv School of Economics（基辅经济学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对战场场景中无人机声学检测面临的噪声、域偏移等问题，提出含PCEN与注意力池化的鲁棒框架及域感知训练策略，在前线数据集上将F1分数从55.4%提升至78.6%。
AI中文摘要：
被动声学传感为小型无人机检测提供了一种关键、高性价比且至关重要的被动替代方案。然而，极端环境噪声和异构硬件导致的传感器诱导域偏移阻碍了声学系统的实际部署。本文针对真实战场条件设计了一种鲁棒框架以应对这些挑战，提出整合逐通道能量归一化（PCEN）与注意力池化，以在低信噪比场景下增强特征提取；还提出一种域感知训练策略，利用辅助类别和多麦克风数据缓解跨域性能下降。在来自乌克兰前线的独特作战区域录音数据集上进行评估，本文方法显著优于现有基准，将F1分数从55.4%提升至78.6%。本文内容最初发表于由信息系统技术（IST）科学技术委员会组织、2026年5月12-13日在英国巴斯举办的军事通信与信息系统国际会议（ICMCIS）。
英文摘要：
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.
