Detection-Aware Delayed Multi-Sensor Fusion for UAV GNSS Spoofing Mitigation: A Factor-Graph-Based Framework

GNSS spoofing can progressively corrupt unmanned aerial vehicle (UAV) navigation when manipulated position solutions are fused before integrity assessment. Existing detection methods often use dead-reckoning references such as visual–inertial odometry (VIO) without fully accounting for accumulated drift or GNSS-dependent global alignment. This paper presents a closed-loop software-in-the-loop (SITL) evaluation platform and a detection-aware delayed multi-sensor fusion framework for UAV GNSS spoofing mitigation. The platform couples state estimation, flight control, vehicle dynamics, and sensor feedback to enable Monte Carlo evaluation under realistic navigation–control interactions. A cumulative sum (CUSUM) detector monitors consistency between GNSS and drift-compensated VIO, while recent GNSS factors are quarantined and incorporated into a factor graph only after integrity screening. Experiments quantify false-alarm rate, detection probability, and latency across detector settings and spoofing deviation rates. Onboard implementation and controlled injection experiments further assess real-time feasibility and hardware transferability. Under the evaluated sensor conditions and gradual-drift attack model, a 10–15 s window yielded detection probabilities exceeding 96% for attacks with a deviation rate of 0.5 m/s, with an approximately 3% false-alarm rate. These findings demonstrate the framework’s potential for spoofing mitigation and the platform’s utility for closed-loop evaluation.