Distributed robust moving horizon estimation for multisensor systems with stochastic and norm-bounded uncertainties

In this study, a distributed robust moving horizon estimation (DRMHE) approach is proposed for time-varying multisensor systems affected by stochastic and norm-bounded uncertainties. First, by considering the uncertainties in all model matrices, stochastic min-max optimization problems in local and collective forms are presented to achieve the proposed estimator. Then, by converting the problems to robust regularized least-squares problems with uncertain parameters, closed-form solutions are obtained for estimations in both local and collective forms. Furthermore, the stability of the proposed estimator is investigated under some appropriate conditions. At last, two different examples are employed to show the robust performance and superiority of the proposed DRMHE approach compared to the existing methods.

Automated summary

In this study, a distributed robust moving horizon estimation (DRMHE) approach is proposed for time-varying multisensor systems affected by stochastic and norm-bounded uncertainties. The approach formulates stochastic min-max optimization problems and converts them into robust regularized least-squares problems using uncertain parameters. Closed-form solutions are obtained for estimations and the stability of the proposed estimator is investigated. Two examples are used to demonstrate the robust performance and superiority of the proposed DRMHE approach compared to existing methods.