无人机搭载麦克风阵列，实现高效声学搜救与目标定位
Sky-Ear: An Unmanned Aerial Vehicle-Enabled Victim Sound Detection and Localization System
- 用环形麦克风阵列采集飞行中的声音数据
- 双阶段处理：先检测后优化定位方向，提升精度
- 适合灾害搜救场景，尤其适用于资源受限的无人机
无人机在搜救任务中应用日益广泛，但受机载硬件限制，持续可靠的遇险人员声源检测与定位仍具挑战。本文设计了一种名为「Sky-Ear」的无人机声学感知系统，实现节能高效的音频传感与声音检测。该系统采用环形麦克风阵列，在飞行过程中持续录制音频。提出两阶段音频处理机制（哨兵阶段与响应阶段）：哨兵阶段基于掩码自编码器（MAE）分析时频域声学特征；为提高定位精度，通过多视角观测优化检测方向，实现连续定位。大量仿真实验验证了系统在遇险人员检测准确率与定位误差方面的性能表现。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in search-and-rescue (SAR) missions, yet continuous and reliable victim detection and localization remain challenging due to on-board hardware constraints. This paper designs an UAV-Enabled Victim Sound Detection and Localization System (called ``Sky-Ear'' for brevity) to achieve energy-efficient acoustic sensing and sound detection for SAR. Sky-Ear enables the ``ear'' of the UAV with a circular-shaped microphone array, and the array conducts continuous audio recordings during the UAV's flight. In Sky-Ear, a two-stage (Sentinel and Responder) audio processing method is developed for energy-consuming and highly reliable sound detection. In the Sentinel stage, a Masking autoencoder (MAE)-based sound detection mechanism is designed to analyze frequency-time acoustic features. For improved precision, a continuous localization method is designed by optimizing detected directions from multiple observations. Extensive simulation experiments are conducted to validate the system's performance in terms of victim detection accuracy and localization error.
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