The work presented here solves the multi-sensor centralized fusion problem in the linear Gaussian model without the measurement noise variance. We generalize the variational Bayesian approximation based adaptive Kalman filter (VB_AKF) from the single sensor filtering to a multi-sensor fusion system, and propose two new centralized fusion algorithms, i.e., VB_AKF-based augmented centralized fusion algorithm and VB_AKF-based sequential centralized fusion algorithm, to deal with the case that the measurement noise variance is unknown. The simulation results show the effectiveness of the proposed algorithms.


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    Titel :

    Multi-Sensor Centralized Fusion without Measurement Noise Covariance by Variational Bayesian Approximation


    Beteiligte:
    Xinbo Gao, (Autor:in) / Jinguang Chen, (Autor:in) / Dacheng Tao, (Autor:in) / Xuelong Li, (Autor:in)


    Erscheinungsdatum :

    01.01.2011


    Format / Umfang :

    2134843 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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