This article considers the problem of distributed robust state estimation for sensor networks in the presence of model uncertainty and multiplicative noise. More precisely, we assume that the modeling uncertainty, i.e., the actual state space model belongs to an ambiguity set or a set of convex polytopic uncertain parameters. Several robust Kalman filters are proposed based on projection theorem, variance-constrained optimization, and robust mean square error estimation with different types of ambiguity sets. Stability analysis and simulation example verify the presented distributed robust filters.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Distributed Robust Kalman Filters Under Model Uncertainty and Multiplicative Disturbance


    Contributors:
    Yu, Xingkai (author) / Li, Jianxun (author)


    Publication date :

    2023-04-01


    Size :

    1346250 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Robust centralized fusion time-varying Kalman filters

    Qi, Wenjuan / Sheng, Zunbing / Shen, Cong | IEEE | 2018


    Kalman and smooth variable structure filters for robust estimation

    Gadsden, Stephen Andrew / Habibi, Saeid / Kirubarajan, Thia | IEEE | 2014



    Polynomial Kalman Filters

    Zarchan, Paul / Musoff, Howard | AIAA | 2015


    Polynomial Kalman Filters

    Musoff, Howard / Zarchan, Paul | AIAA | 2009