  As radar sensors are being miniaturized, there is a growing interest for
using them in indoor sensing applications such as indoor drone obstacle
avoidance. In those novel scenarios, radars must perform well in dense scenes
with a large number of neighboring scatterers. Central to radar performance is
the detection algorithm used to separate targets from the background noise and
clutter. Traditionally, most radar systems use conventional CFAR detectors but
their performance degrades in indoor scenarios with many reflectors. Inspired
by the advances in non-linear target detection, we propose a novel
high-performance, yet low-complexity target detector and we experimentally
validate our algorithm on a dataset acquired using a radar mounted on a drone.
We experimentally show that our proposed algorithm drastically outperforms
OS-CFAR (standard detector used in automotive systems) for our specific task of
indoor drone navigation with more than 19% higher probability of detection for
a given probability of false alarm. We also benchmark our proposed detector
against a number of recently proposed multi-target CFAR detectors and show an
improvement of 16% in probability of detection compared to CHA-CFAR, with even
larger improvements compared to both OR-CFAR and TS-LNCFAR in our particular
indoor scenario. To the best of our knowledge, this work improves the state of
the art for high-performance yet low-complexity radar detection in critical
indoor sensing applications.
