RIEKF-based resilient interactive fusion algorithm for cooperative navigation
Fujun Song, Qinghua Zeng, Rui Zhang, Zhu Xiaohu, Huan Zhou, Xiaoyu Ye · Measurement Science and Technology · 2025
Abstract Multisource information plays a pivotal role in improving the precision and robustness of cooperative navigation. However, effectively coordinating the roles of diverse submodels and maintaining algorithm stability and accuracy under disturbances and faults present significant challenges. To address these challenges, this study proposes a multi-source resilient interactive fusion algorithm for cooperative navigation based on error-state interactive multiple model-right-invariant extended Kalman filtering (IMM-RIEKF). The proposed framework is designed to enhance the convergence, precision, and robustness of the algorithm in disturbed environments. First, leveraging the IMM strategy, an efficient dimensionless multisource credibility evaluation system is developed to highlight the synergistic interactions among submodels. The system pioneers the application of credibility analysis to redundant inertial measurement units and measurement information. Additionally, this study introduces a multisource robust fusion framework grounded in Lie Group Invariant Kalman Filtering. By establishing credibility metrics to guide the design of the model transition probability matrix, the system reduces the sensitivity of cooperative navigation submodel weights, thereby improving the algorithm’s convergence and precision under disturbances. To confirm the feasibility and robustness of the IMM-RIEKF algorithm, experiments were designed at the redundant Unmanned Aerial Vehicle platform. Results reveal that the proposed algorithm offers substantial improvements in robustness and accuracy over the error-state RIEKF and extended state extended Kalman filter.