Monte Carlo Modelling of Echoes Reflected by High‐Rise Architectural Landmarks in UAV Anticollision Radar

This paper presents a novel approach to synthesising radar echoes for unmanned aerial vehicle (UAV) anticollision systems, specifically focusing on the challenges posed by high‐rise architectural landmarks in urban environments. We employ a Monte Carlo method to generate synthetic radar data that accurately reflects the statistical properties of real‐world radar echoes, derived from data collected using a custom‐designed X‐band radar. Our methodology involves the probabilistic modelling of radar echoes for three distinct classes: large‐scale arena building, sky‐scraping slender spire and background noise, using kernel density estimation (KDE). This approach allows for the creation of a large database of synthetic radar signatures essential for training and validating machine learning algorithms intended for use in UAV collision avoidance systems. The results demonstrate the efficacy of our method in preserving the statistical characteristics of real radar data, enabling the generation of high‐fidelity synthetic echoes that can significantly enhance the development and testing of UAV navigation and obstacle avoidance systems.