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). 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 commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays.

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跟踪的独创性方法。室内无人机定位与跟踪无疑是一个关键且尚未完全解决的挑战：在GPS拒止环境中，现有方法适用性有限（尤其在非视距条件下），或需要大规模环境设备部署，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征来估计其位置并推演运动状态，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抑制位置估计误差。基于商用麦克风阵列实现了AIM系统，并在多种场景下使用商用无人机评估其性能。实验结果表明，在复杂室内场景中（现有红外系统因非视距条件完全失效），AIM的平均定位误差比商用超宽带系统降低46%。我们进一步证明，通过部署分布式麦克风阵列，AIM可扩展至任意尺度与布局的室内空间，且不损失定位精度。