부산대학교 도서관

We present Acoustic Inertial Measurement (${\sf AIM}$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, ${\sf AIM}$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 ${\sf AIM}$AIM can support indoor spaces with arbitrary ranges and layouts. We implement ${\sf AIM}$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 ${\sf AIM}$AIM is 46$\mathrm{\%}$% lower than that of commercial UWB-based systems in a complex 10$\,$m$\,$×$\,$10$\,$m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5$\mathrm{m}$m in a 20$\mathrm{m}$m range, and even support spaces with arbitrary ranges and layouts.