Recent events of drones flying over city centers, official buildings and nuclear installations stressed the growing threat of
uncontrolled drone proliferation and the lack of real countermeasure. Indeed, detecting and tracking them can be difficult
with traditional techniques. A system to acoustically detect and track small moving objects, such as drones or ground
robots, using acoustic cameras is presented. The described sensor, is completely passive, and composed of a 120-element
microphone array and a video camera. The acoustic imaging algorithm determines in real-time the sound power level
coming from all directions, using the phase of the sound signals. A tracking algorithm is then able to follow the sound
sources. Additionally, a beamforming algorithm selectively extracts the sound coming from each tracked sound source.
This extracted sound signal can be used to identify sound signatures and determine the type of object.
The described techniques can detect and track any object that produces noise (engines, propellers, tires, etc). It is
a good complementary approach to more traditional techniques such as (i) optical and infrared cameras, for which the
object may only represent few pixels and may be hidden by the blooming of a bright background, and (ii) radar or other
echo-localization techniques, suffering from the weakness of the echo signal coming back to the sensor. The distance of
detection depends on the type (frequency range) and volume of the noise emitted by the object, and on the background
noise of the environment. Detection range and resilience to background noise were tested in both, laboratory environments
and outdoor conditions. It was determined that drones can be tracked up to 160 to 250 meters, depending on their type.
Speech extraction was also experimentally investigated: the speech signal of a person being 80 to 100 meters away can be
captured with acceptable speech intelligibility.