Design of a Drone Platform for Sensor Fusion Data Acquisition

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 constant false alarm rate (CFAR) detectors, but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in nonlinear target detection, this chapter proposes a novel high performance yet low-complexity target detector and experimentally validates the proposed algorithm on a dataset acquired using a radar mounted on a drone. It is experimentally shown that the proposed algorithm drastically outperforms ordered statistics CFAR (OS-CFAR) for the specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. After introducing this novel radar detector, this chapter goes on to present how the proposed radar sensing setup is embedded on a drone platform for acquiring sensor-fusion databases that will be used in the subsequent chapter during various SLAM and people detection experiments.