© 2020 IEEE.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 (RCs) 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.