  With the development and widespread use of wireless devices in recent years
(mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has
become extremely crowded. In order to counter security threats posed by rogue
or unknown transmitters, it is important to identify RF transmitters not by the
data content of the transmissions but based on the intrinsic physical
characteristics of the transmitters. RF waveforms represent a particular
challenge because of the extremely high data rates involved and the potentially
large number of transmitters present in a given location. These factors outline
the need for rapid fingerprinting and identification methods that go beyond the
traditional hand-engineered approaches. In this study, we investigate the use
of machine learning (ML) strategies to the classification and identification
problems, and the use of wavelets to reduce the amount of data required. Four
different ML strategies are evaluated: deep neural nets (DNN), convolutional
neural nets (CNN), support vector machines (SVM), and multi-stage training
(MST) using accelerated Levenberg-Marquardt (A-LM) updates. The A-LM MST method
preconditioned by wavelets was by far the most accurate, achieving 100%
classification accuracy of transmitters, as tested using data originating from
12 different transmitters. We discuss strategies for extension of MST to a much
larger number of transmitters.
