Integrity Monitoring Algorithm for Low-Altitude UAV GNSS/INS Tightly Coupled Navigation Based on Factor Graph Optimization

With the rapid development and widespread use of low-altitude unmanned aerial vehicles (UAVs), the frequency of faults has increased, posing significant risks to public infrastructure and personal safety. In this context, accurate and reliable navigation information has become increasingly critical for ensuring flight safety and effectively addressing faults. Therefore, this paper proposes a fault detection and integrity monitoring algorithm for GNSS/INS tightly coupled navigation based on factor graph optimization (FGO), aimed at providing dynamic and precise UAV navigation information to ensure UAV management and public safety. Firstly, the pseudorange, carrier phase, and Doppler frequency measurements from GNSS are fully utilized to construct the GNSS/INS tightly coupled navigation algorithm based on FGO. Then, real-time residual and cumulative residual chi-square test statistics are constructed using the sliding window. Simulation tests are conducted under different GNSS step fault, GNSS slope fault and IMU fault cases. Compared to traditional integrity monitoring algorithms for integrated navigation systems based on Kalman Filter (KF), the proposed algorithm effectively reduces the false alarm rate and missed detection rate of faults. Additionally, a fault isolation algorithm is designed, which can distinguish between GNSS and IMU faults and accurately isolate the faulty satellites. Finally, the protection level calculated by the algorithm effectively bounds the positioning error in missed detection scenarios. The simulation results demonstrate that the proposed algorithm can accurately detect faults and isolate faulty satellite, thereby ensuring the reliability of the UAV positioning system.