The article examines modern methods for recognizing and analyzing radio signals with partial and complete spectral overlap, which represent one of the key and most complex problems in the field of radio monitoring, telecommunications, and technical intelligence. It describes the theoretical foundations and practical aspects of applying the Fast Fourier Transform method, the principal component analysis method, and the independent component analysis method for separating, identifying, and classifying signals in complex conditions. The fast Fourier transform method demonstrated high efficiency in processing partially overlapping signals, as it enables the determination of main frequency components even at a low signal-to-noise ratio and in the presence of significant interference. For complete spectral overlap, a combined approach is proposed that integrates principal component analysis and independent component analysis, providing preliminary signal decorrelation and subsequent separation according to the statistical independence criterion. A key improvement is the introduction of the spectral entropy criterion, which is based on assessing the level of randomness of the signal’s energy distribution in the frequency domain. High entropy values indicate significant noisiness or random structure, while low values indicate the presence of pronounced frequency components. Using this criterion makes it possible to automatically select the most informative components, removing insignificant noise components and reducing computational costs. A series of numerical experiments was carried out, a quantitative assessment of signal separation accuracy was performed, and the influence of noise level and degree of spectrum overlap on the final results was analyzed. The proposed approach can be adapted for a wide range of tasks, including automated technical monitoring systems, radio intelligence systems, and tools for detecting low-visibility signals. The results confirm the feasibility of its use in adaptive next-generation digital signal processing systems and its potential for the development of intelligent radio monitoring algorithms.
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