RAIL: Robust Acoustic Indoor Localization for Drones

TL;DR

RAIL is a new ultrasonic-based indoor localization system for drones that achieves high accuracy in GPS-denied environments by overcoming multi-path effects with a hybrid FH-CDMA technique, validated through simulations and experiments.

Contribution

The paper introduces RAIL, a novel hybrid FH-CDMA scheme for ultrasonic localization that improves accuracy and robustness in indoor drone navigation without GPS.

Findings

Achieves less than 1.5 cm average localization error.

Effectively overcomes multi-path fading effects.

Demonstrates high accuracy in real-world experiments.

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.