Geometric Modeling of UAV Air-to-Ground Sensing Channels Using 77 GHz FMCW Radar

Unmanned Aerial Vehicle air-to-ground (UAV-A2G) radar sensing channels are critical for 6 G integrated sensing and communication systems, yet their geometric characteristics and multipath behavior remain insufficiently characterized. This paper presents a comprehensive measurement campaign utilizing a 77 GHz Frequency Modulated Continuous Wave (FMCW) radar mounted on a lightweight unmanned aerial vehicle (UAV) platform (2.6 kg) in a cluttered campus environment. Multipath components are extracted via the space-alternating generalized expectation-maximization algorithm (SAGE), and a novel trajectory association framework integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering with fuzzy C-means (FCM) and Kuhn-Munkres (KM) algorithms enables robust multipath tracking. Furthermore, this paper proposes a hyperbolic geometric model. By analyzing range-time domain signatures, the model distinguishes real targets from multipath-induced ghost targets, providing crucial insights for UAV-based perception systems and advancing radar channel modeling in high-frequency bands.