Computational Aspects of Sensor Fusion for GNSS Outlier Mitigation in Navigation

Abstract

As the Global Navigation Satellite Systems (GNSS) are intensively used as main source of Position, Navigation and Timing (PNT) information for maritime and inland water navigation, it becomes increasingly important to ensure the reliability of GNSS-based navigation solutions for challenging environments. Although an intensive work has been done in developing GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithms, a reliable procedure to mitigate multiple simultaneous outliers is still lacking. The presented work evaluates the performance of several methods for multiple outlier mitigation based on robust estimation framework and compares them to the performance of state-of-the-art methods. The relevant methods include M-estimation, S-estimation, Least Median of Squares LMS-based approaches as well as corresponding modifications for C/N0-based weighting schemes. The snapshot positioning methods are also tested within the quaternion-based Unscented Kalman filter for integrated inertial/GNSS solution. The proposed schemes are evaluated using real measurement data from challenging inland water scenarios with multiple bridges and a waterway lock. The initial results are encouraging and clearly indicate the potential of the discussed methods both for classical snapshot solutions as well for the methods with complementary sensors.