Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm

Abstract

To address the issue of low positioning accuracy in unmanned vehicles navigating in obstructed spaces due to easily contaminated navigation measurement information, an improved adaptive federated Kalman filtering INS/GNSS/VNS integrated navigation algorithm is proposed. In this algorithm, an inertial navigation system (INS) serves as the common reference system, and, together with the global navigation satellite system (GNSS) and visual navigation system (VNS), they form the subsystems that together make up the main system. In the event of faulty measurement values in the subsystems, a combination of the residual chi-square and sliding-window averaging methods are used for fault detection to improve the fault tolerance of the integrated navigation algorithm. Additionally, an adaptive sharing factor is proposed to adjust the accuracy of the integrated navigation algorithm based on the accuracy of the sub-filters. Simulation experiments demonstrated that, compared with classic federated Kalman filtering, the proposed algorithm reduced the root mean square errors (RMSEs) of the three-dimensional position by 56.4%, 54.8%, and 43.4% and the root mean square errors of the three-dimensional velocity by 71.0%, 72.1%, and 28.4% in the event of sub-filter faults, effectively solving the problem of low positioning accuracy for unmanned vehicles in obstructed spaces while ensuring the real-time performance of the system.