Fault-tolerant relative navigation based on Kullback–Leibler divergence. (29th December 2020)

• Record Type: Journal Article
• Authors: Xiong, Jun; Cheong, Joon Wayn; Xiong, Zhi; Dempster, Andrew G; Tian, Shiwei; Wang, Rong; Liu, Jianye
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 6(2020:Nov./Dec.)
• Publication Date: 2020-12-29
• Subjects: Fault detection and exclusion -- extended Kalman filter -- Kullback–Leibler divergence -- relative navigation; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420979125
• Languages: English
• ISSNs: 1729-8806

Abstract

A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2 -based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F -distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2 -based method.