  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.
