利用声音和惯性数据实现室内无人机高精度定位，无需复杂布设。
Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement
- 通过麦克风阵列捕捉无人机声学特征，结合卡尔曼滤波与四分位距去噪
- 在10米×10米复杂环境中的定位误差比商用UWB低46%
- 适用于非视距场景，支持任意布局空间，适合智能仓储、巡检等应用
本文提出声学惯性测量（AIM）技术，用于解决室内无人机定位与追踪难题。在无GPS信号环境下，现有方法受限于视距要求、需大量环境布设或对无人机硬件软件有较高改动需求。相比之下，AIM利用无人机声学特性估计位置并推导运动轨迹，即使在非视距（NLoS）条件下仍有效。通过专用卡尔曼滤波与四分位距规则（IQR）抑制定位误差，实验证明其可在任意布局的室内空间中稳定工作。使用市售麦克风阵列与商用无人机测试，在10m×10m复杂场景下，平均定位误差比商用超宽带（UWB）系统降低46%，且红外系统因受阻无法工作时，该方法仍可运行。部署分布式麦克风阵列后，20米范围内平均误差可降至0.5米以下，具备强适应性。
原文摘要 · Abstract (English)
We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m\times10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.
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