  Micro Air Vehicles (MAVs) are increasingly being used for complex or
hazardous tasks in enclosed and cluttered environments such as surveillance or
search and rescue. With this comes the necessity for sensors that can operate
in poor visibility conditions to facilitate with navigation and avoidance of
objects or people. Radar sensors in particular can provide more robust sensing
of the environment when traditional sensors such as cameras fail in the
presence of dust, fog or smoke. While extensively used in autonomous driving,
miniature FMCW radars on MAVs have been relatively unexplored. This study aims
to investigate to what extent this sensor is of use in these environments by
employing traditional signal processing such as multi-target tracking and
velocity obstacles. The viability of the solution is evaluated with an
implementation on board a MAV by running trial tests in an indoor environment
containing obstacles and by comparison with a human pilot, demonstrating the
potential for the sensor to provide a more robust sense and avoid function in
fully autonomous MAVs.
