High-Rise Architectural Landmarks Detection and Identification by Spatio-Probabilistic Models for UAV Anti-Collision Radar—A Real Data Case

Abstract

Unmanned aerial vehicles (UAVs) heavily rely on GPS, a system vulnerable to signal interference in complex urban environments. Although radar systems offer a robust alternative due to their ability to effectively penetrate adverse weather and operate in darkness, a key challenge remains: reliably identifying static architectural landmarks from sparse and noisy radar echoes. This paper proposes a novel method for creating spatio-probabilistic models (SPMs) of radar echoes from high-rise urban landmarks, enabling independent, radar-based UAV localisation. We employ kernel density estimation on real radar data, acquired with a custom-designed X-band ENAVI radar, focusing on large arena buildings and slender spires. These SPMs are then used to detect and identify landmarks by calculating the divergence between the probability distributions of the real-time received echoes and the preestimated reference models. Our evaluation, using probabilistic divergence metrics on Wrocław's Centennial Hall and Iglica, shows that this method effectively preserves the statistical properties of the radar data, generating high-fidelity SPMs. This approach significantly improves landmark identification compared to classical correlation methods, paving the way for more robust and resilient UAV navigation systems.