Applying Hyperbolic Representations of Radio Frequency Spectra for UAV Classification with the Kolmogorov-Arnold Model: An Efficiency Assessment

The rapid advancement of unmanned aerial vehicle (UAV) technologies has heightened the demand for reliable detection methods. Traditional approaches, such as radar, often fail due to the use of low-reflectivity materials and quiet motors in modern drones. Radio frequency (RF) spectral analysis presents a promising alternative by enabling UAV detection and classification through the analysis of power spectra in the 2.4 GHz and 5.8 GHz bands, commonly used for video signal transmission. However, RF spectral analysis faces significant challenges, including interference from industrial noise and other high-frequency sources, as well as signal distortion in complex environments. To address these issues, artificial intelligence (AI) and machine learning (ML) techniques can enhance UAV identification by recognizing unique spectral patterns. This study explores the application of hyperbolic representations of RF spectra as a preprocessing step for spectral data, further analyzed using the advanced Kolmogorov-Arnold model. This approach aims to improve the accuracy and efficiency of UAV detection systems based on video channel signals.