Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

2024-06-01

IEEE Transactions on Mobile Computing

AI Summary

To address the challenge of precise localization and tracking of indoor drones under GPS-denied and non-line-of-sight (NLoS) conditions, this paper proposes a hardware-agnostic acoustic-inertial fusion method that requires no drone hardware modification or large-scale infrastructure deployment. Innovatively leveraging the drone’s rotor-generated acoustic signatures as motion sources, the approach integrates a distributed microphone array with a customized extended Kalman filter (EKF) and incorporates an interquartile range (IQR)-based robust outlier rejection mechanism. This enables real-time, three-dimensional pose estimation and motion tracking in arbitrarily sized and configured indoor environments. Experimental evaluation in a complex 10 m × 10 m indoor setting demonstrates that the method achieves a 46% reduction in mean localization error compared to a commercial ultra-wideband (UWB) system; moreover, positioning accuracy remains within 0.5 m across a 20 m operational range.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present Acoustic Inertial Measurement (<inline-formula><tex-math notation="LaTeX">${sf AIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AIM</mml:mi></mml:math><inline-graphic xlink:href="he-ieq1-3335860.gif"/></alternatives></inline-formula>), 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, <inline-formula><tex-math notation="LaTeX">${sf AIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AIM</mml:mi></mml:math><inline-graphic xlink:href="he-ieq2-3335860.gif"/></alternatives></inline-formula> exploits the acoustic characteristics of the drones to estimate their location and derive their motion, <italic>even in NLoS</italic> settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that <inline-formula><tex-math notation="LaTeX">${sf AIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AIM</mml:mi></mml:math><inline-graphic xlink:href="he-ieq3-3335860.gif"/></alternatives></inline-formula> can support indoor spaces with arbitrary ranges and layouts. We implement <inline-formula><tex-math notation="LaTeX">${sf AIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AIM</mml:mi></mml:math><inline-graphic xlink:href="he-ieq4-3335860.gif"/></alternatives></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">${sf AIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">AIM</mml:mi></mml:math><inline-graphic xlink:href="he-ieq5-3335860.gif"/></alternatives></inline-formula> is 46<inline-formula><tex-math notation="LaTeX">$mathrm{%}$</tex-math><alternatives><mml:math><mml:mo>%</mml:mo></mml:math><inline-graphic xlink:href="he-ieq6-3335860.gif"/></alternatives></inline-formula> lower than that of commercial UWB-based systems in a complex 10<inline-formula><tex-math notation="LaTeX">$,$</tex-math><alternatives><mml:math><mml:mspace width="0.166667em"/></mml:math><inline-graphic xlink:href="he-ieq7-3335860.gif"/></alternatives></inline-formula>m<inline-formula><tex-math notation="LaTeX">$,$</tex-math><alternatives><mml:math><mml:mspace width="0.166667em"/></mml:math><inline-graphic xlink:href="he-ieq8-3335860.gif"/></alternatives></inline-formula>×<inline-formula><tex-math notation="LaTeX">$,$</tex-math><alternatives><mml:math><mml:mspace width="0.166667em"/></mml:math><inline-graphic xlink:href="he-ieq9-3335860.gif"/></alternatives></inline-formula>10<inline-formula><tex-math notation="LaTeX">$,$</tex-math><alternatives><mml:math><mml:mspace width="0.166667em"/></mml:math><inline-graphic xlink:href="he-ieq10-3335860.gif"/></alternatives></inline-formula>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<inline-formula><tex-math notation="LaTeX">$mathrm{m}$</tex-math><alternatives><mml:math><mml:mi mathvariant="normal">m</mml:mi></mml:math><inline-graphic xlink:href="he-ieq11-3335860.gif"/></alternatives></inline-formula> in a 20<inline-formula><tex-math notation="LaTeX">$mathrm{m}$</tex-math><alternatives><mml:math><mml:mi mathvariant="normal">m</mml:mi></mml:math><inline-graphic xlink:href="he-ieq12-3335860.gif"/></alternatives></inline-formula> range, and even support spaces with arbitrary ranges and layouts.

Problem

• Indoor drone localization in GPS-denied environments
• Tracking drones in Non-Line of Sight (NLoS) conditions
• Reducing hardware/software changes for drone localization

Innovation

• Uses acoustic characteristics for drone localization
• Employs Kalman filter and IQR for error reduction
• Works in NLoS with off-the-shelf microphone array