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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    Title :

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


    Contributors:
    Xinbo Gao, (author) / Jinguang Chen, (author) / Dacheng Tao, (author) / Xuelong Li, (author)


    Publication date :

    2011-01-01


    Size :

    2134843 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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