Drone detection by cognitive passive radar using 5g signals

In this thesis, Support Vector Machine, KNN and Decision Tree Algorithms are used to compare the capacity of Cognitive Passive Radar using 5th Generation New Radio Communication signals for drone and human-being detection and classification. Additionally, the human walking simulator and propeller simulator by V.Chen are examined and modified. Then, a cheap and compact Cognitive Software Defined Radio hardware is developed and tested for Passive Radar applications. Developed Passive Radar system is tested and measured for different types of drones and human walking patterns using 3.5 GHz and 35 GHz 5th Generation New Radio Signals at 5G Open Test Area belonging to Information and Communication Technologies Authority.