  The applications of the unmanned aerial vehicles (UAVs) increase rapidly in
everyday life, thus detecting the UAVs and/or its pilot is a crucial task. Many
UAVs adopt frequency hopping spread spectrum (FHSS) technology to efficiently
and securely communicate with their radio controllers (RC) where the signal
follows a hopping pattern to prevent harmful interference. In order to
realistically distinguish the frequency hopping (FH) RC signals, one should
consider the real-world radio propagation environment since many UAVs
communicate with RCs from a far distance in which signal faces both slow and
fast fading phenomenons. Therefore, in this study different from the
literature, we consider a system that works under real-conditions by capturing
over-the-air signals at hilly terrain suburban environments in the presence of
foliages. We adopt the short-time Fourier transform (STFT) approach to capture
the hopping sequence of each signal. Furthermore, time guards associated with
each hopping sequence are calculated using the autocorrelation function (ACF)
of the STFT which results in differentiating the each UAV RC signal accurately.
In order to validate the performance of the proposed method, the results of
normalized mean square error (MSE) respect to different signal-to-noise ratio
(SNR), window size and Tx-Rx separation values are given.
