We present an information theoretic approach to develop an interacting multiple model (IMM) estimator. In the mixing and output steps of the proposed estimator, the weighted Kullback-Leibler (KL) divergence is used to derive the fusion of conditional probability density functions. A lower bound and an upper bound are derived for the error covariance of controllable and observable Markov jump linear systems. Simulation results are provided to verify the effectiveness of the proposed estimator.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An information theoretic approach to interacting multiple model estimation


    Beteiligte:
    Wenling Li, (Autor:in) / Yingmin Jia, (Autor:in)


    Erscheinungsdatum :

    2015-07-01


    Format / Umfang :

    1394108 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vehicle and road state estimation using interacting multiple model approach

    Tsunashima, H. / Murakami, M. / Miyata, J. et al. | British Library Conference Proceedings | 2006


    Interacting multiple model road curvature estimation

    Shen, Truman / Ibrahim, Faroog | IEEE | 2012


    Vehicle and road state estimation using interacting multiple model approach

    Tsunashima, H. / Murakami, M. / Miyataa, J. | Taylor & Francis Verlag | 2006


    Vehicle and road state estimation using interacting multiple model approach

    Tsunashima,H. / Murakami,M. / Miyata,J. et al. | Kraftfahrwesen | 2006


    Vehicle and Road State Estimation by Interacting Multiple Model Approach (20065486)

    Tsunashima, H. / Murakami, M. / Miyata, J. et al. | British Library Conference Proceedings | 2006