GNSS Receiver Autonomous Integrity Monitoring Algorithm Based on Robust Strong Tracking Filtering

In this paper, the effects of Kalman model error and gross errors in measurement data on the performance of RAIM (Receiver Autonomous Integrity Monitoring) are studied. Firstly, the deviations of RAIM detection statistic caused by the above errors are calculated. And then an improved RAIM detection algorithm based on robust strong tracking filtering is proposed. The robust strong tracking filtering can reduce the adverse effects by using the adaptive fading factors of strong tracking filtering and robust Kalman filtering simultaneously. Finally, the detection capability of RAIM is verified after injecting the model error and satellite fault. Experiment results show that the improved RAIM algorithm is robust in dealing with the errors. Moreover, when there is a satellite fault, its detection value is more accurate and stable. After the fault disappears, the improved algorithm converges more quickly.