Kalman filtering with scheduled measurements - part I : estimation framework
Date of Issue2012
World Congress on Intelligent Control and Automation (10th : 2012 : Beijing, China)
School of Electrical and Electronic Engineering
This paper proposes an estimation framework under scheduled measurements for linear discrete-time stochastic systems. Both controllable and uncontrollable schedulers are considered. Under a controllable scheduler, only the normalized measurement innovation greater than a threshold will be communicated to the estimator. While under an uncontrollable scheduler, the time duration between consecutive sensor communications is triggered by an independent and identically distributed process. For both types of scheduler, recursive estimators that achieve the minimum mean square estimation error are derived, respectively. Moreover, necessary and sufficient conditions for stability of the mean square estimation error are provided.
DRNTU::Engineering::Electrical and electronic engineering
© 2012 IEEE.