  We present a high-fidelity Mixed Reality sensor emulation framework for
testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs)
against false data injection (FDI) attacks. The proposed approach can be
utilized to assess the impact of FDI attacks, benchmark attack detector
performance, and validate the effectiveness of mitigation/reconfiguration
strategies in single-UAV and UAV swarm operations. Our Mixed Reality framework
leverages high-fidelity simulations of Gazebo and a Motion Capture system to
emulate proprioceptive (e.g., GNSS) and exteroceptive (e.g., camera) sensor
measurements in real-time. We propose an empirical approach to faithfully
recreate signal characteristics such as latency and noise in these
measurements. Finally, we illustrate the efficacy of our proposed framework
through a Mixed Reality experiment consisting of an emulated GNSS attack on an
actual UAV, which (i) demonstrates the impact of false data injection attacks
on GNSS measurements and (ii) validates a mitigation strategy utilizing a
distributed camera network developed in our previous work. Our open-source
implementation is available at
\href{https://github.com/CogniPilot/mixed\_sense}{\texttt{https://github.com/CogniPilot/mixed\_sense}}
