Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

We present Acoustic Inertial Measurement (
${\sf AIM}$
), 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,
${\sf AIM}$
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
${\sf AIM}$
can support indoor spaces with arbitrary ranges and layouts. We implement
${\sf AIM}$
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
${\sf AIM}$
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
$\mathrm{m}$
in a 20
$\mathrm{m}$
range, and even support spaces with arbitrary ranges and layouts.