具有相关噪声的事件触发序贯融合估计

Event-triggered sequential fusion estimation with correlated noises.

作者信息

Yan Liping, Jiang Lu, Xia Yuanqing, Jonathan Wu Q M

机构信息

Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; University of Windsor, Windsor N9B3P4, Canada.

School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.

出版信息

ISA Trans. 2020 Jul;102:154-163. doi: 10.1016/j.isatra.2019.07.029. Epub 2019 Aug 5.

DOI:10.1016/j.isatra.2019.07.029

PMID:31399253

Abstract

This paper focuses on the event-triggered sequential fusion estimation for the multi-sensor systems with correlated noises. An event-triggered communication mechanism is introduced to reduce unnecessary energy waste. Considering that measurement noise of different sensors is correlated with each other and also correlated with the system noise of the previous step, an event-triggered sequential fusion estimation algorithm is proposed in the sense of linear minimum covariance. The standard values of the correlation parameters are defined to ensure convergence of the designed fusion algorithm and an upper bound of the estimation error covariance is given. A numerical example is used to illustrate the effectiveness of the presented fusion algorithm.

摘要

本文聚焦于具有相关噪声的多传感器系统的事件触发序贯融合估计。引入了一种事件触发通信机制以减少不必要的能量浪费。考虑到不同传感器的测量噪声相互关联且与上一步的系统噪声也相关，在线性最小协方差意义下提出了一种事件触发序贯融合估计算法。定义了相关参数的标准值以确保所设计融合算法的收敛性，并给出了估计误差协方差的上界。通过一个数值例子说明了所提出融合算法的有效性。