PROJECT TITLE :

On Set-Valued Kalman Filtering and Its Application to Event-Based State Estimation

ABSTRACT:

Motivated by challenges in state estimation with event-primarily based measurement updates, the properties of the exact and approximate set-valued Kalman filters with multiple sensor measurements for linear time-invariant systems are investigated during this work. 1st, we have a tendency to show that the exact and the proposed approximate set-valued filters are independent of the fusion sequence at every time instant. Second, the boundedness of the size of the set of estimation means that is proved for the precise set-valued filter. For the approximate set-valued filter, if the closed-loop matrix is contractive, then the set of estimation means that encompasses a bounded size asymptotically; otherwise a nonsingular linear rework is made such that the size of the set of estimation means for the remodeled states is asymptotically bounded. Third, the effect of set-valued measurements on the size of the set of estimation means is analyzed and conditions for performance improvement in terms of smaller size of the set of estimation means that are proposed. Finally, the results are applied to event-primarily based estimation, which enable the event-triggering conditions to be designed by considering needs on performance and communication rates. The efficiency of the proposed results are illustrated by simulation examples and comparison with the approximate event-based MMSE estimator and the Kalman filter with intermittent observations.