A GNSS PPP integrity monitoring algorithm based on ARKF and MHSS

Satellite navigation integrity monitoring techniques are essential to ensure navigation safety in aviation. The availability of precise point positioning (PPP) integrity monitoring is challenged by the introduction of hazardously misleading information (HMI) influenced by receiver measurement noise as well as complex application scenarios. Aiming at this problem, this study proposed a PPP integrity monitoring algorithm based on adaptive robust Kalman filter and multiple hypothesis solution separation (ARKF-MHSS). It fully integrates the robustness and adaptivity of ARKF with the fault detection and processing capability of MHSS, and introduces an equivalent covariance matrix to reduce the influence of observation noise on the position solution results, which effectively improves the availability of PPP integrity monitoring in complex environments. The effectiveness of the ARKF-MHSS algorithm in airborne PPP scenarios is fully verified by conducting static evaluations based on International GNSS Service (IGS) stations and dynamic evaluations based on general aviation flight tests. The experimental results show that in the static test, the stricter the alert limit (AL), the more significant the improvement of the ARKF-MHSS algorithm. When the HAL is set to 3 m and 2.5 m, the average improvement in availability is 0.43 % and 13.13 %, respectively. In the flight test, the algorithm can effectively meet the positioning performance requirements of the LPV-200 (Localizer Performance with Vertical guidance) approach phase, and the availability reaches 100 %. In the Category I precision approach (CAT-I) phase (VAL = 10 m), the availability reaches 98.2 %.