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arXiv · 2111.03764

RAIL: Robust Acoustic Indoor Localization for Drones

Alireza Famili·Angelos Stavrou·Haining Wang·Jung-Min·Park
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Abstract

Navigating in environments where the GPS signal is unavailable, weak, purposefully blocked, or spoofed has become crucial for a wide range of applications. A prime example is autonomous navigation for drones in indoor environments: to fly fully or partially autonomously, drones demand accurate and frequent updates of their locations. This paper proposes a Robust Acoustic Indoor Localization (RAIL) scheme for drones designed explicitly for GPS-denied environments. Instead of depending on GPS, RAIL leverages ultrasonic acoustic signals to achieve precise localization using a novel hybrid Frequency Hopping Code Division Multiple Access (FH-CDMA) technique. Contrary to previous approaches, RAIL is able to both overcome the multi-path fading effect and provide precise signal separation in the receiver. Comprehensive simulations and experiments using a prototype implementation demonstrate that RAIL provides high-accuracy three-dimensional localization with an average error of less than $1.5$~cm.

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Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min, Park. 2021-11-06. RAIL: Robust Acoustic Indoor Localization for Drones. https://arxiv.org/abs/2111.03764

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