CNN–Transformer-Enhanced GNSS/RISS Integrated Navigation Algorithm Based on RISS Recomputed Method

Continuous navigation information for dynamic road vehicles is commonly provided through integrated Inertial Navigation System (INS)/Global Navigation Satellite System (GNSS) solutions. By using fewer inertial sensors and reducing computational requirements, the Reduced Inertial Sensor System (RISS) is a highly suitable alternative to INS for vehicular navigation applications. This article proposes a CNN–Transformer-enhanced GNSS/RISS integrated navigation algorithm based on the RISS Recomputed Method (RRM). Specifically, the RISS Recomputed Method is first used to mitigate RISS errors. Moreover, the CNN and Transformer models are adopted to further estimate the RISS error model, thereby improving observation accuracy and reducing navigation errors during GNSS outages. Finally, the feasibility and effectiveness of the proposed approach are evaluated through land-vehicle navigation experiments. The experimental results demonstrate that the proposed CNN–Transformer algorithm based on RRM can improve positioning accuracy and robustness in complex land-vehicle environments.