提升战场环境下无人机声学探测精度，应对噪声和设备差异挑战
Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels
- 用通道能量归一化与注意力池化增强低信噪比下的特征提取
- 引入辅助类别和多麦克风数据，使模型跨设备场景性能提升23.2个百分点
- 适用于真实战场环境的弱标签数据训练，适合军事安防与反无人机系统研发
被动声学感知为探测小型无人飞行器提供了一种关键、低成本且隐蔽的替代方案。然而，极端环境噪声及异构硬件引发的传感器域偏移严重制约了其实际部署。本文提出一个面向真实战场条件的鲁棒框架，结合每通道能量归一化（PCEN）与基于注意力的池化机制，提升低信噪比下的特征提取能力；同时设计域感知训练策略，利用辅助类别和多麦克风数据缓解跨域性能下降问题。在乌克兰前线作战区录制的独特数据集上评估，所提方法将F1分数从55.4%显著提升至78.6%，优于现有基线。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。