Ni Yuqing, Liu Xiaochen, Yang Chao
The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China.
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Sensors (Basel). 2023 Oct 24;23(21):8667. doi: 10.3390/s23218667.
This paper proposes a time- and event-triggered hybrid scheduling for remote state estimation with limited communication resources. A smart sensor observes a physical process and decides whether to send the local state estimate to a remote estimator via a wireless communication channel; the estimator computes the state estimate of the process according to the received data packets and the known scheduling mechanism. Based on the existing optimal time-triggered scheduling, we employ a stochastic event trigger to save precious communication chances and further improve the estimation performance. The minimum mean-squared error (MMSE) state estimate is derived since the Gaussian property is preserved. The estimation performance upper bound and communication rate are analyzed. The main results are illustrated by numerical examples.
本文针对通信资源受限的远程状态估计问题，提出了一种时间触发与事件触发相结合的混合调度方法。智能传感器观测物理过程，并决定是否通过无线通信信道将本地状态估计值发送给远程估计器；估计器根据接收到的数据包和已知的调度机制计算过程的状态估计值。基于现有的最优时间触发调度，我们采用随机事件触发来节省宝贵的通信机会，并进一步提高估计性能。由于保留了高斯特性，推导了最小均方误差（MMSE）状态估计。分析了估计性能上界和通信速率。通过数值算例说明了主要结果。
