A new direct filtering approach to INS/GNSS integration

This paper presents a novel direct filtering approach to INS/GNSS (Inertial Navigation System / Global Navigation Satellite System) integration. This approach establishes a kinematic model for INS/GNSS integration by combining inertial navigation equations and IMU (Inertial Measurement Unit) error equations. Subsequently, a refined strong tracking unscented Kalman filter (RSTUKF) is developed to enhance the UKF robustness against kinematic model error. This RSTUKF adopts the strategy of assumption test to identify kinematic model error. Based on this, a suboptimal fading factor (SFF) is derived and embedded in the predicted covariance to weaken the influence of prior information on the filtering solution only in the presence of kinematic model error. In addition to correction of the UKF estimation in the presence of kinematic model error, the RSTUKF also maintains the optimal UKF estimation in the absence of kinematic model error. Simulation and experimental analysis demonstrate the performance of the proposed approach to INS/GNSS integration.