With the development of information fusion and filtering theory, we can find a lot of research achievements on target tracking and signal processing. For autoregressive (AR) signal system with multisensor and correlated white noises, if its noise variance and model parameter are unknown or not given, a self-adjusting fused Wiener filter is proposed. The solution to the problem is to substitute estimated values of the unkown elements for real values. The next thing to do is to prove the convergence performance in realization of the self-adjusting fuser obtained above. The DESA (dynamic error system analysis) is made to prove the asymptotic optimality of the self-adjusting fused Wiener filter and checked by a simulation example.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-adjusting Information Fusion Wiener Filter for the Multisensor Signal Systems with Correlated Noise


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Yi (Herausgeber:in) / Martinsen, Kristian (Herausgeber:in) / Yu, Tao (Herausgeber:in) / Wang, Kesheng (Herausgeber:in) / Liu, Jinfang (Autor:in) / Liu, Lei (Autor:in) / Gao, Yannan (Autor:in) / Zhang, Peng (Autor:in) / Liu, Yao (Autor:in)

    Kongress:

    International Workshop of Advanced Manufacturing and Automation ; 2020 ; Zhanjiang, China October 12, 2020 - October 13, 2020



    Erscheinungsdatum :

    2021-01-23


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







    Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter

    Yuan Gao, / Wen-Jing Jia, / Xiao-Jun Sun, et al. | IEEE | 2009


    Optimum multisensor fusion of correlated local decisions

    Drakopoulos, E. / Lee, C.-C. | IEEE | 1991