Generally, the extended Kalman filter (EKF) is used for sensor fusion in a land vehicle navigation system. However, defects of the first-order linearization of the nonlinear model in the EKF can introduce large estimated errors, and may lead to sub-optimal performance. In order to yield higher accuracy of navigation, in this paper, a novel particle filter (PF) for sensor fusion is proposed and the sampling importance resampling particle filter (SIR-PF) is applied to address the nonlinear measurement model and it shows better performances when compared with the EKF. The basic theories and application of the general PF and the SIR-PF for a global position system/dead reckoning (GPS/DR) integrated navigation system are discussed.


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    Title :

    Particle filter for sensor fusion in a land vehicle navigation system


    Contributors:

    Published in:

    Publication date :

    2005


    Size :

    5 Seiten, 4 Bilder, 2 Tabellen, 17 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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