Collection: UAV-Based RSS Measurements from the AFAR Challenge in Digital Twin and Real-World Environments
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918149439356928 |
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| author | Masrur, Saad Ozdemir, Ozgur Gurses, Anil Guvenc, Ismail Sichitiu, Mihail L. Dutta, Rudra Mushi, Magreth Zajkowski, homas Dickerson, Cole Reddy, Gautham Villar, Sergio Vargas Wong, Chau-Wai Chatterjee, Baisakhi Chaudhari, Sonali Li, Zhizhen Liu, Yuchen Kudyba, Paul Sun, Haijian Mandapaka, Jaya Sravani Namuduri, Kamesh Wang, Weijie Fund, Fraida |
| author_facet | Masrur, Saad Ozdemir, Ozgur Gurses, Anil Guvenc, Ismail Sichitiu, Mihail L. Dutta, Rudra Mushi, Magreth Zajkowski, homas Dickerson, Cole Reddy, Gautham Villar, Sergio Vargas Wong, Chau-Wai Chatterjee, Baisakhi Chaudhari, Sonali Li, Zhizhen Liu, Yuchen Kudyba, Paul Sun, Haijian Mandapaka, Jaya Sravani Namuduri, Kamesh Wang, Weijie Fund, Fraida |
| contents | This paper presents a comprehensive real-world and Digital Twin (DT) dataset collected as part of the AERPAW Find A Rover (AFAR) Challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) testbed and hosted at the Lake Wheeler Field in Raleigh, North Carolina. The AFAR Challenge was a competition involving five finalist university teams, focused on promoting innovation in unmanned aerial vehicle (UAV)-assisted radio frequency (RF) source localization. Participating teams were tasked with designing UAV flight trajectories and localization algorithms to detect the position of a hidden unmanned ground vehicle (UGV), also referred to as a rover, emitting probe signals generated by GNU Radio. The competition was structured to evaluate solutions in a DT environment first, followed by deployment and testing in the AERPAW outdoor wireless testbed. For each team, the UGV was placed at three different positions, resulting in a total of 29 datasets, 15 collected in a DT simulation environment and 14 in a physical outdoor testbed. Each dataset contains time-synchronized measurements of received signal strength (RSS), received signal quality (RSQ), GPS coordinates, UAV velocity, and UAV orientation (roll, pitch, and yaw). Data is organized into structured folders by team, environment (DT and real-world), and UGV location. The dataset supports research in UAV-assisted RF source localization, air-to-ground (A2G) wireless propagation modeling, trajectory optimization, signal prediction, autonomous navigation, and DT validation. With 300k time-synchronized samples from the real-world experiments, the AFAR dataset enables effective training/testing of deep learning (DL) models and supports robust, real-world UAV-based wireless communication and sensing research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collection: UAV-Based RSS Measurements from the AFAR Challenge in Digital Twin and Real-World Environments Masrur, Saad Ozdemir, Ozgur Gurses, Anil Guvenc, Ismail Sichitiu, Mihail L. Dutta, Rudra Mushi, Magreth Zajkowski, homas Dickerson, Cole Reddy, Gautham Villar, Sergio Vargas Wong, Chau-Wai Chatterjee, Baisakhi Chaudhari, Sonali Li, Zhizhen Liu, Yuchen Kudyba, Paul Sun, Haijian Mandapaka, Jaya Sravani Namuduri, Kamesh Wang, Weijie Fund, Fraida Signal Processing Audio and Speech Processing This paper presents a comprehensive real-world and Digital Twin (DT) dataset collected as part of the AERPAW Find A Rover (AFAR) Challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) testbed and hosted at the Lake Wheeler Field in Raleigh, North Carolina. The AFAR Challenge was a competition involving five finalist university teams, focused on promoting innovation in unmanned aerial vehicle (UAV)-assisted radio frequency (RF) source localization. Participating teams were tasked with designing UAV flight trajectories and localization algorithms to detect the position of a hidden unmanned ground vehicle (UGV), also referred to as a rover, emitting probe signals generated by GNU Radio. The competition was structured to evaluate solutions in a DT environment first, followed by deployment and testing in the AERPAW outdoor wireless testbed. For each team, the UGV was placed at three different positions, resulting in a total of 29 datasets, 15 collected in a DT simulation environment and 14 in a physical outdoor testbed. Each dataset contains time-synchronized measurements of received signal strength (RSS), received signal quality (RSQ), GPS coordinates, UAV velocity, and UAV orientation (roll, pitch, and yaw). Data is organized into structured folders by team, environment (DT and real-world), and UGV location. The dataset supports research in UAV-assisted RF source localization, air-to-ground (A2G) wireless propagation modeling, trajectory optimization, signal prediction, autonomous navigation, and DT validation. With 300k time-synchronized samples from the real-world experiments, the AFAR dataset enables effective training/testing of deep learning (DL) models and supports robust, real-world UAV-based wireless communication and sensing research. |
| title | Collection: UAV-Based RSS Measurements from the AFAR Challenge in Digital Twin and Real-World Environments |
| topic | Signal Processing Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.06823 |