This chapter is concerned with the robust filtering problem for a class of nonlinear stochastic systems with missing measurements and parameter uncertainties. The missing measurements are modeled by a Bernoulli distributed white sequence with a known conditional probability distribution. The nonlinearities considered in this chapter are expressed by the statistical means and could cover several well studied nonlinearities as special cases. Based on this model, the robust variance‐constrained filtering problem is addressed for a class of nonlinear stochastic systems with missing measurements. It is the objective to design a filter such that, for all parameter uncertainties and possible measurements missing, 1) the filtering error system is exponentially mean‐square stable and 2) the variance of the estimation error for individual state is not more than prescribed upper bound. It is shown that the solvability of the addressed filtering problem can be expressed as the feasibility of a certain set of linear matrix inequalities, and the explicit expression of the desired robust filters is also derived.


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    Title :

    Robust Variance‐Constrained Filtering with Missing Measurements


    Contributors:
    Ma, Lifeng (author) / Wang, Zidong (author) / Bo, Yuming (author)


    Publication date :

    2015-03-16


    Size :

    26 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English