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.


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    Titel :

    A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression



    Erschienen in:

    The journal of navigation ; 69 , 4 ; 905-919


    Erscheinungsdatum :

    2016




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    53.84 Ortungstechnik, Radartechnik / 55.44 Schiffsführung / 53.84 / 42.89 / 55.20 / 55.44 / 55.86 / 55.54 / 55.86 Schiffsverkehr, Schifffahrt / 55.20 Straßenfahrzeugtechnik / 42.89 Zoologie: Sonstiges / 55.54 Flugführung
    Lokalklassifikation TIB:    275/5680/7035




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