Bridging Simulation and Reality: A 3D Clustering-Based Deep Learning Model for UAV-Based RF Source Localization

Localization of radio frequency (RF) sources has critical applications, including search and rescue, jammer detection, and monitoring of hostile activities. Unmanned aerial vehicles (UAVs) offer significant advantages for RF source localization (RFSL) over terrestrial methods, leveraging autonomous 3D navigation and capturing stronger signals at higher altitudes. Recent advancements in deep learning (DL) have further enhanced localization accuracy, particularly for outdoor scenarios. DL models often face challenges in real-world deployment due to training on simulated datasets that do not fully replicate real-world conditions. To address this, we first propose the Enhanced Two-Ray propagation model, reducing the simulation-to-reality gap by improving the accuracy of propagation environment modeling. For RFSL, we propose the 3D Cluster-Based RealAdap-tRNet, a DL-based method that leverages 3D clustering-based feature extraction for robust localization. Experimental results demonstrate that the proposed Enhanced Two-Ray model provides superior accuracy in simulating real-world propagation scenarios. Notably, the 3D Cluster-Based RealAdaptRNet, trained entirely on simulated data, achieves exceptional performance when validated in a real-world setting using the AERPAW physical testbed, with an average localization error of 18.2 m. The proposed approach is computationally efficient—using 33.5× fewer parameters—and generalizes well across diverse trajectories, making it highly suitable for real-world applications.