  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.
