An Inertial-Based Integrated Navigation Robust Filtering Algorithm Based on Vehicle Model for GNSS Failure

The accuracy and robustness of strapdown inertial navigation system (SINS)/Global Navigation Satellite System (GNSS) integrated navigation are crucial in autonomous driving navigation applications. However, under the conditions of urban high-rise buildings and roadside trees, as well as the limitations of the GNSS, the traditional robust algorithm design at the filtering level makes it difficult to meet the needs of the actual vehicle movement environment. We developed a GNSS-independent, inertial-based navigation technology called vehicle model-assisted inertial-based integrated navigation robust filtering algorithm. Without adding external navigation sensors, the vehicle motion characteristics are modeled, and dead reckoning observations are used to assist SINS status updates. The more flexible federal unscented Kalman filter (UKF) framework is designed considering vehicle kinematics and dynamics for nonlinear navigation systems. Our strategy will provide reliable observable physical quantities. The simulation test results show that the accuracy and robustness of the low-cost vehicle inertial-based integrated navigation system are improved. This new method reduces the heading angle error by 84% and the corresponding position error by 96% during GNSS outages, as demonstrated in this article. The effectiveness of the algorithm proposed in this article was verified.