  In this work, we performed a thorough comparative analysis on a radio
frequency (RF) based drone detection and identification system (DDI) under
wireless interference, such as WiFi and Bluetooth, by using machine learning
algorithms, and a pre-trained convolutional neural network-based algorithm
called SqueezeNet, as classifiers. In RF signal fingerprinting research, the
transient and steady state of the signals can be used to extract a unique
signature from an RF signal. By exploiting the RF control signals from unmanned
aerial vehicles (UAVs) for DDI, we considered each state of the signals
separately for feature extraction and compared the pros and cons for drone
detection and identification. Using various categories of wavelet transforms
(discrete wavelet transform, continuous wavelet transform, and wavelet
scattering transform) for extracting features from the signals, we built
different models using these features. We studied the performance of these
models under different signal to noise ratio (SNR) levels. By using the wavelet
scattering transform to extract signatures (scattergrams) from the steady state
of the RF signals at 30 dB SNR, and using these scattergrams to train
SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.
