A SINS/GNSS/VDM Integrated Navigation Fault-Tolerant Mechanism Based on Adaptive Information Sharing Factor

Autonomous driving vehicle is a kind of unmanned vehicle on the ground. Navigation and positioning technology is becoming an indispensable part of practical applications, especially in intelligent transportation systems and military fields. In this study, multisource information fusion technology plays a necessary role in enhancing the quality and reliability of navigation and positioning. In the actual complex environment, the accuracy requirements of vehicle navigation and positioning in the case of nonlinear systems and non-Gaussian distribution are fully considered. In this article, a new adaptive sharing factor integrated navigation information fusion technology scheme is designed. From a geometric point of view, the VDM is studied, and new VDM observation information that fully considers the turning process of the vehicle is established. It combines strapdown inertial navigation system (SINS), global navigation satellite system (GNSS), and VDM multisource sensors, and a VDM and GNSS measurement model based on nonintegrity constraints is developed. The performance of the local filter is adaptively reflected by the F-norm adaptive information sharing factor algorithm established based on the variance. At the same time, the development process of a federated filter fault diagnosis algorithm based on unscented Kalman filter (UKF) is reported, which has fault diagnosis and information recovery functions. The independent characteristics of the subfilters are considered, and a fault-tolerant mechanism for state chi-square detection is established. Both GNSS and VDM are used as observation sources to improve the accuracy of low-cost SINS. This strategy is implemented on the basis of Carsim professional vehicle dynamics software. The evaluation test indicated that the algorithm has high navigation accuracy, high fault tolerance, and guaranteed system reliability, which is verified in this article.