Active propulsion noise shaping for multi-rotor aircraft localization

Tamir Shor, Gabriele Serussi, Tom Hirshberg, Chaim Baskin, Alex M. Bronstein

Keywords: Localization, Acoustics, Deep Learning

TL;DR: Methodological development of system for drone localization based on self-emitted propulsion sound.

Abstract:

— Multi-rotor aerial autonomous vehicles (MAVs)
primarily rely on vision for navigation purposes. However,
visual localization and odometry techniques suffer from poor
performance in low or direct sunlight, a limited field of view,
and vulnerability to occlusions. Acoustic sensing can serve as
a complementary or even alternative modality for vision in
many situations, and it also has the added benefits of lower
system cost and energy footprint, which is especially important
for micro aircraft. This paper proposes actively controlling and
shaping the aircraft propulsion noise generated by the rotors to
benefit localization tasks, rather than considering it a harmful
nuisance. We present a neural network architecture for selfnoise-based localization in a known environment. We show
that training it simultaneously with learning time-varying rotor
phase modulation achieves accurate and robust localization.
The proposed methods are evaluated using a computationally
affordable simulation of MAV rotor noise in 2D acoustic
environments that is fitted to real recordings of rotor pressure
fields.

Submission Number: 115