A new Bayesian approach for multiple satellite faults detection and exclusion is proposed by introducing a classification variable to each satellite observation. If we treat this classification variable as random and assume a prior distribution for it, then a rule for satellite fault detection and exclusion based on the posterior probabilities of the classification variables is constructed under the framework of Bayesian hypothesis testing. Secondly, the Gibbs sampler is introduced to compute the posterior probabilities of the classification variables. Then the implementation for a Bayesian Receiver Autonomous Integrity Monitoring (RAIM) algorithm is designed with the Gibbs sampler. Finally, different schemes are designed to evaluate the performance of the new Bayesian RAIM algorithm in the case of multiple faults. We compare the method in this paper with the Range Consensus (RANCO) method. Experiments illustrate that the proposed algorithm in this paper is capable of detecting and eliminating multiple satellite faults, and the probability of correctly detecting faults is high.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A new Bayesian RAIM for Multiple Faults Detection and Exclusion in GNSS


    Contributors:

    Published in:

    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

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



    An Improved RAIM Algorithm for Multiple Satellite Faults Detection and Exclusion

    Wang, Mengchen / Li, Zhimin / Lai, Jizhou et al. | Springer Verlag | 2021


    An Improved RAIM Algorithm for Multiple Satellite Faults Detection and Exclusion

    Wang, Mengchen / Li, Zhimin / Lai, Jizhou et al. | TIBKAT | 2022


    RAIM WITH MULTIPLE FAULTS

    Angus, J.E. | Online Contents | 2006


    Multi GNSS Advanced RAIM: An availability analysis

    Muhammad, Bilal / Cianca, Ernestina / Salonico, Antonio Maria | IEEE | 2014


    Kalman filter–based RAIM for GNSS receivers

    Bhattacharyya, Susmita / Gebre-Egziabher, Demoz | IEEE | 2015