Enhancing Situational Awareness in ISAC Networks via Drone Swarms: A Real-World Channel Sounding Data Set

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.

Bistatic Micro-Doppler Analysis of a Vertical Takeoff and Landing (VTOL) Drone in ICAS Framework

Integrated Communication and Sensing (ICAS) is a key technology that enables sensing functionalities within the next-generation mobile communication (6G). Joint design and optimization of both functionalities could allow coexistence, therefore it advances toward joint signal processing and using the same hardware platform and common spectrum. Contributing to ICAS sensing, this paper presents the measurement and analysis of the micro-Doppler signature of Vertical Takeoff and Landing (VTOL) drones. Measurement is performed with an OFDM-like communication signal and bistatic constellation, which is a typical case in ICAS scenarios. This work shows that micro-Doppler signatures can be used to precisely distinguish flight modes, such as take-off, landing, hovering, transition, and cruising.