  The use of supervised learning with various sensing techniques such as audio,
visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been
widely applied in the detection of unmanned aerial vehicles (UAV) in an
environment. However, little or no attention has been given to the application
of unsupervised or semi-supervised algorithms for UAV detection. In this paper,
we proposed a semi-supervised technique and architecture for detecting UAVs in
an environment by exploiting the RF signals (i.e., fingerprints) between a UAV
and its flight-controller communication under wireless inference such as
Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet
packet transform, we estimated the second moment statistic (i.e., variance) of
the coefficients in each packet as a feature set. We developed a local outlier
factor model as the UAV detection algorithm using the coefficient variances of
the wavelet packets from WiFi and Bluetooth signals. When detecting the
presence of RF-based UAV, we achieved an accuracy of 96.7$\%$ and 86$\%$ at a
signal-to-noise ratio of 30~dB and 18~dB, respectively. The application of this
approach is not limited to UAV detection as it can be extended to the detection
of rogue RF devices in an environment.
