A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression. (26th January 2016)

• Record Type: Journal Article
• Authors: Zhu, Yixian; Cheng, Xianghong; Wang, Lei
• Is Part Of: Journal of navigation. Volume 69:Number 4(2016)
• Page Start: 905
• Page End: 919
• Publication Date: 2016-01-26
• Subjects: Fault detection, -- Gaussian process regression, -- Integrated navigation system, -- Particle swarm optimisation; Navigation -- Periodicals; 623.8905
• DOI: 10.1017/S0373463315001034
• Languages: English
• ISSNs: 0373-4633

Abstract

Abstract : For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.