Abstract
This chapter describes in detail a new method for the detection and identification of unmanned aerial vehicles (UAVs) based on the radio‐frequency (RF) signals and the corresponding RF fingerprints. It also describes a multistage UAV RF signal detection technique in the presence of noise and radio interference. The chapter explains the feature extraction and the RF fingerprinting‐based UAV classification system. The proposed detection system operates in the presence of wireless interference from WiFi and Bluetooth sources. These interference signals are detected using a multistage detector that estimates the bandwidth and modulation features of the detected signals. Once the signal from a UAV controller is detected, it is identified using different machine learning algorithms. The experimental setup together with the results of the detection and classification of UAV RF signals are also described.
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