A Novel Fault Detection Framework-Based Extend Kalman Filter for Fault-Tolerant Navigation System

Abstract

Global navigation satellite systems (GNSS) often suffer from service interruptions or multipath errors in urban canyon environments, giving rise to reduced navigation accuracy. Therefore, it is necessary to develop effective fault-tolerant navigation systems to ensure a high-level accuracy despite GNSS failures. In this article, we present a novel fault detection framework based on the extended Kalman filter to address the problem of untimely fault detection and inaccurate positioning when GNSS fails. Specifically, we introduce the statistical process control technique of control charts to address the issue of slow-varying fault detection by constructing kernel multivariate exponentially weighted moving-average control charts instead of the conventional chi-square test. Simultaneously, we establish a corresponding criterion using EWMA-related statistics to mitigate the negative impact of uncertain noise and abnormal innovation, thereby ensuring the positioning accuracy of the navigation system. Finally, we validate the effectiveness and superiority of the proposed method through simulations and vehicle field data, demonstrating its ability to detect anomalies promptly and enhance the navigation and positioning accuracy while mitigating the adverse effects of GNSS lapse.