Deep learning based doppler radar for micro UAS detection and classification

In this paper, a radar sensor is proposed for the automated detection and classification of micro unmanned aerial systems (UASs), using Doppler signatures and their spectral correlation functions (SCFs). Our proposed system effectively detects and identifies UASs (within the radar beam width) by employing a Deep Belief Network (DBN) to classify the SCF signature patterns. The proposed system is experimentally verified using 3 UASs sensed with a 2.4 GHz continuous-wave doppler radar, which is set up in a laboratory environment. The experiment results show that a Doppler radar sensor is able to detect and classify UASs with an accuracy above 90% based on the automated classification of the radar signature SCF using DBN-based classifier.