Co-Localization Method Based on Robust IEKF with Adaptive RCST
Yao Fu, Jizhou Lai, Pin Lyu, Zhaolong Wu, Qinghe Lu, Qieqie Zhang · 2024
In order to solve the problem of decreased navigation accuracy caused by outliers in cooperative navigation, this paper proposes an iterative extended Kalman filter method based on adaptive fault tolerance for a GNSS/INS tightly combined master-slave UAV cluster. This method first adds residual chi-square test method in the iterative process of the iterative extended Kalman filter to improve the fault tolerance of the system in the face of measurement outliers; then, according to the influence of different degrees of outliers on measurement and filtering, two thresholds and corresponding adaptive fault tolerance parameters are designed for the residual chi-square test method; finally, a system simulation model with four masters and one slave is established, and the algorithm in this paper is used for combined cooperative navigation filtering. The simulation results show that the proposed algorithm can effectively improve the system's cooperative positioning accuracy and enhance the robustness of the cooperative navigation system.