  With the upcoming multitude of commercial and public applications envisioned
in the mobile 6G radio landscape using unmanned aerial vehicles (UAVs),
integrated sensing and communication (ISAC) plays a key role to enable the
detection and localization of passive objects with radar sensing, while
optimizing the utilization of scarce resources. To explore the potential of
future ISAC architectures with UAVs as mobile nodes in distributed multi-sensor
networks, the system's fundamental capability to detect static and dynamic
objects that reveal themselves by their bi-static back-scattering needs to be
evaluated. Therefore, this paper addresses simulation- and measurement based
data acquisition methods to gather knowledge about the bistatic reflectivity of
single objects including their Micro-Doppler signature for object
identification as well as the influence of multipath propagation in different
environments on the localization accuracy and radar tracking performance. We
show exemplary results from simulation models, bi-static reflectivity
measurements in laboratory environment and real-flight channel sounding
experiments in selected scenarios showcasing the potential of synthetic and
measured data sets for development and evaluation of ISAC algorithms. The
presented measurement data sets are publicly available to encourage the
academic RF community to validate future algorithms using realistic scenarios
alongside simulations models.
