Enhancing Situational Awareness in ISAC Networks via Drone Swarms: A Real-World Channel Sounding Data Set
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arXiv
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| Format: | Preprint |
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2025
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| author | Beuster, Julia Andrich, Carsten Giehl, Sebastian Miranda, Marc Mohr, Lorenz Novotny, Dieter Kaufmann, Tom Schneider, Christian Thomä, Reiner |
| author_facet | Beuster, Julia Andrich, Carsten Giehl, Sebastian Miranda, Marc Mohr, Lorenz Novotny, Dieter Kaufmann, Tom Schneider, Christian Thomä, Reiner |
| contents | With the upcoming capabilities of integrated sensing and communication (ISAC) and the incorporation of user equipment (UE) like unmanned aerial vehicles (UAVs) in 6G mobile networks, there is a significant opportunity to enhance situational awareness through multi-static radar sensing in meshed ISAC networks. This paper presents a real-world channel sounding data set acquired using a testbed with synchronized, distributed ground-based sensor nodes and flying sensor nodes within a swarm of up to four drones. The conducted measurement campaign is designed to sense the bi-static reflectivity of objects such as parking cars, vertical take-off and landing (VTOL) aircraft, and small drones in multi-path environments. We detail the rationale behind the selection of the included scenarios and the configuration of the participating nodesand present exemplary results to demonstrate the potential of using collaborating drone swarms for multi-static radar tracking and localization in air-to-air (A2A) and air-to-ground (A2G) scenarios. The data sets are publicly available to support the development and validation of future ISAC algorithms in real-world environments rather than relying solely on simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Situational Awareness in ISAC Networks via Drone Swarms: A Real-World Channel Sounding Data Set Beuster, Julia Andrich, Carsten Giehl, Sebastian Miranda, Marc Mohr, Lorenz Novotny, Dieter Kaufmann, Tom Schneider, Christian Thomä, Reiner Signal Processing With the upcoming capabilities of integrated sensing and communication (ISAC) and the incorporation of user equipment (UE) like unmanned aerial vehicles (UAVs) in 6G mobile networks, there is a significant opportunity to enhance situational awareness through multi-static radar sensing in meshed ISAC networks. This paper presents a real-world channel sounding data set acquired using a testbed with synchronized, distributed ground-based sensor nodes and flying sensor nodes within a swarm of up to four drones. The conducted measurement campaign is designed to sense the bi-static reflectivity of objects such as parking cars, vertical take-off and landing (VTOL) aircraft, and small drones in multi-path environments. We detail the rationale behind the selection of the included scenarios and the configuration of the participating nodesand present exemplary results to demonstrate the potential of using collaborating drone swarms for multi-static radar tracking and localization in air-to-air (A2A) and air-to-ground (A2G) scenarios. The data sets are publicly available to support the development and validation of future ISAC algorithms in real-world environments rather than relying solely on simulation. |
| title | Enhancing Situational Awareness in ISAC Networks via Drone Swarms: A Real-World Channel Sounding Data Set |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.12010 |