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A UAV-assisted Wireless Localization Challenge on AERPAW
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As wireless researchers are tasked to enable wireless communication as infrastructure in more dynamic aerial settings, there is a growing need for large-scale experimental platforms that provide realistic, reproducible, and reliable experimental validation. To bridge the research-to-implementation gap, the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) offers open-source tools, reference experiments, and hardware to facilitate and evaluate the development of wireless research in controlled digital twin environments and live testbed flights. The inaugural AERPAW Challenge, "Find a Rover," was issued to spark collaborative efforts and test the platform's capabilities. The task involved localizing a narrowband wireless signal, with teams given ten minutes to find the "rover" within a twenty-acre area. By engaging in this exercise, researchers can validate the platform's value as a tool for innovation in wireless communications research within aerial robotics. This paper recounts the methods and experiences of the top three teams in automating and rapidly locating a wireless signal by automating and controlling an aerial drone in a realistic testbed scenario.
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Cited by 1 Pith paper
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Bridging Simulation and Reality: A 3D Clustering-Based Deep Learning Model for UAV-Based RF Source Localization
A UAV-borne deep learning model trained only on simulated radio data localizes a real RF source with 18.2 m average error on the AERPAW testbed, using a new enhanced two-ray propagation model and 3D clustering.
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