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.

翻译：本文提出声学惯性测量（AIM）技术，这是一种用于室内无人机定位与追踪的独特方法。室内无人机定位与追踪无疑是一个关键且尚未解决的挑战：在无GPS环境中，现有方法适用性有限（尤其在非视距条件下），需要广泛的环境设备部署，或要求对无人机进行显著的硬件/软件改造。相比之下，AIM利用无人机的声学特性来估计其位置并推导其运动轨迹，即使在非视距环境中也能实现。我们通过专用的卡尔曼滤波器与四分位距规则（IQR）抑制位置估计误差，并证明AIM能够适用于任意范围与布局的室内空间。我们使用现成的麦克风阵列实现了AIM系统，并在多种设置下使用商用无人机评估其性能。结果表明，在复杂的10米×10米室内场景中（由于非视距条件，最先进的红外系统甚至无法工作），AIM的平均定位误差比基于商用超宽带系统的方案低46%。当部署分布式麦克风阵列时，在20米范围内平均误差可降至0.5米以下，并能支持任意范围与布局的空间。