FRACTALS REVOLUTIONIZE RADAR BY UNVEILING SUPERIOR SIGNAL PROCESSING TECHNIQUES

General Background: Fractal radiophysics and fractal radio electronics represent advanced fields focusing on the creation and development of innovative radio systems. Specific Background: The integration of fractals, textures, fractional operators, and nonlinear dynamics into radiophysics and radiolocation is an emerging area of study with substantial potential. Knowledge Gap: Despite significant advancements, there remain critical gaps in understanding the application of these elements to improve radiophysics and radar signal processing. Aims: This study aims to bridge these gaps by employing long-term natural experiments, statistical analyses, and new information technologies to enhance the identification and classification of land covers and to develop advanced radar maps. Results: The findings demonstrate the effectiveness of fractal methods in improving the accuracy of radar signal processing and land cover classification, with fractal-based approaches outperforming traditional methods. The study also introduces novel textural techniques for identifying objects in optical and radar images, even under low signal-to-background conditions. Additionally, the feasibility of generating synthetic optical and radar images through stochastic autoregressive synthesis has been theoretically justified and experimentally validated, achieving a physical accuracy of 90%. Novelty: This research is the first to explore the combined textural and spatial spectral-correlation features of optical and radar images for land cover classification, highlighting the superiority of fractal-based approaches. Implications: The results provide a robust theoretical foundation for the practical application of fractal radiolocation and radio electronics, offering new directions for the development of advanced radar technologies and information processing methods, thus contributing to the evolution of modern radiophysics and electronic systems.

Kata Kunci