GNSS Fault Rejection Using Robust Kalman Filter with Integrity Indicator-Constrained Innovation Adaptive Adjustment
Résumé
Global Navigation Satellite System (GNSS) plays an essential role in the infrastructure of many ground transportation mobilities. However, GNSS solutions are vulnerable to harmful reception conditions, such as Non-Line-of-Sight (NLOS) signals and multipath effects. To overcome the negative impact of degraded GNSS solutions in multisensor integration, this paper proposes an Integrity Indicator-Constrained (IIC) GNSS Fault Rejection (FR) method based on Innovation Adaptive Adjustment (IAA). The proposed algorithm integrates a loosely-coupled INS/GNSS/LiDAR multisensor system with a Robust Kalman Filter (RKF) with innovation adaptive adjustment capability. Based on a recursive rule, the fault detection boundary for each measurement updates independently according to the varying operation conditions. The innovations are adjusted dynamically by comparing with this critical boundary constrained by the integrity indicator. The introduction of the integrity indicator can effectively reduce the response latency during environmental transitions. The proposed algorithm has been validated by the real-field vehicular multi-sensor dataset collected in harsh urban environments. The experimental results show this proposed method, i.e., IAA-FR with IIC, can efficiently eliminate faulty GNSS outputs and improve the accuracy and reliability of the navigation solution. The 3D position Root Mean Square Error (RMSE) is improved by approximately 60%, 10%, and 6% compared with those obtained from the traditional KF, the innovation filter, and IAA-FR without IIC respectively.