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

• URL: http://arxiv.org/abs/2502.13969v1
• Date: Sun, 02 Feb 2025 05:48:44 GMT
• Title: Bridging Simulation and Reality: A 3D Clustering-Based Deep Learning Model for UAV-Based RF Source Localization
• Authors: Saad Masrur, Ismail Guvenc,
• Abstract summary: Unmanned aerial vehicles (UAVs) offer significant advantages for RF source localization over terrestrial methods.<n>Recent advancements in deep learning (DL) have further enhanced localization accuracy, particularly for outdoor scenarios.<n>We propose the 3D Cluster-Based RealAdaptRNet, a DL-based method leveraging 3D clustering-based feature extraction for robust localization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 improved signal capture 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 performance, as they are typically trained on simulated datasets that fail to replicate real-world conditions fully. 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 RealAdaptRNet, a DL-based method leveraging 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 compared to conventional free-space and two-ray models. Notably, the 3D Cluster-Based RealAdaptRNet, trained entirely on simulated datasets, achieves exceptional performance when validated in real-world environments using the AERPAW physical testbed, with an average localization error of 18.2 m. The proposed approach is computationally efficient, utilizing 33.5 times fewer parameters, and demonstrates strong generalization capabilities across diverse trajectories, making it highly suitable for real-world applications.

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