Stochastic Event-triggered Sensor Schedule for Remote State Estimation
Abstract
We propose an open-loop and a closed-loop stochastic event-triggered sensor schedule for remote state estimation. Both schedules overcome the essential difficulties of existing schedules in recent literature works where, through introducing a deterministic event-triggering mechanism, the Gaussian property of the innovation process is destroyed which produces a challenging nonlinear filtering problem that cannot be solved unless approximation techniques are adopted. The proposed stochastic event-triggered sensor schedules eliminate such approximations. Under these two schedules, the MMSE estimator and its estimation error covariance matrix at the remote estimator are given in a closed-form. Simulation studies demonstrate that the proposed schedules have better performance than periodic ones with the same sensor-to-estimator communication rate.
Cite
@article{arxiv.1402.0599,
title = {Stochastic Event-triggered Sensor Schedule for Remote State Estimation},
author = {Duo Han and Yilin Mo and Junfeng Wu and Sean Weerakkody and Bruno Sinopoli and Ling Shi},
journal= {arXiv preprint arXiv:1402.0599},
year = {2016}
}