Observability Analysis and Adaptive Information Fusion for Integrated Navigation of Unmanned Ground Vehicles

• Published in: IEEE transactions on industrial electronics (1982) Vol. 67; no. 9; pp. 7659 - 7668
• Main Authors: Shen, Kai, Wang, Meiling, Fu, Mengyin, Yang, Yi, Yin, Zhang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Adaptive information fusion; Adaptive systems; Data integration; Fault tolerance; federated Kalman filter; Global navigation satellite system; Information management; Information sharing; Intelligent transportation systems; Kalman filters; Mathematical model; Navigation systems; Observability; observability analysis; Reliability aspects; Road tests; Sensors; unmanned ground vehicle; Unmanned ground vehicles; Upgrading; Urban areas
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2019.2946564

Integrated navigation of unmanned ground vehicles (UGV) is significant for many advanced intelligent transportation system applications. Adaptive information fusion technique based on observability analysis has a great potential to enhance UGV integrated navigation systems for the capability of high-precision positioning and navigation. In an integrated navigation system, the tolerance against unknown and time-varying observation conditions is a key factor to satisfy the specific requirements of high-precision, self-adaption, and high reliability. Thus, a novel adaptive federated Kalman filter (FKF) is proposed with time-varying information sharing factors based on the criteria for the degree of observability. In addition, an error-state cascaded integration architecture is designed for UGV integrated navigation. Simulation with real datasets gathered from road tests in urban areas showed that the new adaptive integrated navigation system can autonomously update FKF information sharing factors according to the measurement quality and the observability of each navigation error-state. Therefore, the accuracy, robustness, and fault-tolerance ability of the whole system can be effectively improved in a high dynamic environment.