The subject of the research is methods of passive radio engineering differentiation of signals from unmanned aerial vehicles (UAVs) and background sources in the 2.4 GHz band in an intense electronic environment. The aim of the work is to develop and experimentally evaluate an integrated algorithm for detecting and classifying UAV signals based on combining energy criteria, cyclic prefix analysis of OFDM signals, and frequency switching analysis. The research methodology includes a spectral-temporal analysis of broadband recordings obtained using a software defined HackRF One receiver, frequency division of the range into fixed-width sub-bands, calculation of a set of diagnostic features and the formation of a decisive integration rule. Experimental testing of the algorithm was carried out on recordings of UAV signals, Wi-Fi signals, and ISM-band background sources. A statistical assessment of the probability of correct detection and the probability of false alarms was performed using the compiled set of experimental IQ recordings. It is shown that the integration of spectral features provides increased classification stability compared with the use of individual methods, especially in conditions of dense Wi-Fi traffic and multipath propagation. The results obtained can be used in the development of low-cost radio monitoring systems and preliminary detection of the presence of UAVs based on SDR platforms. Keywords : UAV, radio monitoring, time–frequency analysis, radio signal detection, cyclic prefix, OFDM, FHSS, SDR, 2.4 GHz ISM band.