  This paper focuses on the detection and classification of micro-unmanned
aerial vehicles (UAVs) using radio frequency (RF) fingerprints of the signals
transmitted from the controller to the micro-UAV. In the detection phase, raw
signals are split into frames and transformed into the wavelet domain. A Markov
models-based naive Bayes approach is used to check for the presence of a UAV in
each frame. In the classification phase, unlike the traditional approaches that
rely solely on time-domain signals and corresponding features, the proposed
technique uses the energy transient signal. This approach is more robust to
noise and can cope with different modulation techniques. First, the normalized
energy trajectory is generated from the energy-time-frequency distribution of
the raw control signal. Next, the start and end points of the energy transient
are detected by searching for the most abrupt changes in the mean of the energy
trajectory. Then, a set of statistical features is extracted from the energy
transient. Significant features are selected by performing neighborhood
component analysis (NCA) to keep the computational cost of the algorithm low.
Finally, selected features are fed to several machine learning algorithms for
classification. The algorithms are evaluated experimentally using a database
containing 100 RF signals from each of 14 different UAV controllers. The
signals are recorded wirelessly using a high-frequency oscilloscope. The data
set is randomly partitioned into training and test sets for validation with the
ratio 4:1. Ten Monte Carlo simulations are run and results are averaged to
assess the performance of the methods. All the micro-UAVs are detected
correctly and an average accuracy of 96.3% is achieved using the k-nearest
neighbor (kNN) classification. Proposed methods are also tested for different
signal-to-noise ratio (SNR) levels and results are reported.
