Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations

Cong Huang; Peng Mei; Jun Wang

doi:10.1016/j.isatra.2020.10.038

Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations

ISA Trans. 2021 Apr:110:28-38. doi: 10.1016/j.isatra.2020.10.038. Epub 2020 Oct 17.

Authors

Cong Huang¹, Peng Mei², Jun Wang³

Affiliations

¹ School of Information Science and Technology, Donghua University, Shanghai 201620, China; Department of Mechanical Engineering, Politecnicodi Milano, Milan 20156, Italy. Electronic address: cirtis_ch@foxmail.com.
² School of Transportation Science and Engineering, Beihang University, Beijing 100191, China; Department of Mechanical Engineering, Politecnicodi Milano, Milan 20156, Italy. Electronic address: maple@bauu.edu.cn.
³ School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China. Electronic address: ww3422@126.com.

PMID: 33268109
DOI: 10.1016/j.isatra.2020.10.038

Abstract

This novel is concerned with the event-triggering robust fusion estimation problem for multi-rate systems (MRSs) subject to stochastic nonlinearities (SNs) and censored observations (COs). The considered multi-rate system includes several sensor nodes, and each sensor is with different sampling rate. To reflect the dead-zone-like censoring phenomenon, a Tobit-1 regression model with prescribed left-censoring threshold is introduced, and the stochastic nonlinearities characterized by statistical means are considered in the MRSs. In order to save the limited resource, the event-triggering mechanism (ETM) has been introduced to determine whether the specified sensor node should transmit the information to the corresponding local filter. For the addressed MRSs, we aim to design a local Tobit Kalman filtering (TKF) algorithm for each sensor node firstly in the sense of the upper bound on each local filtering error covariance being minimal. Then, such a minimized upper bound is derived by designing the filter gain properly at each iteration. In the sequel, the fusion centre manipulates the local estimates by the CI scheme. Moreover, we discuss the issue of consistency for the proposed multi-rate fusion estimation (MRFE) approach. At last, experimental simulation are exploited to demonstrate the validation of the designed MRFE algorithm.

Keywords: Censored observations (COs); Event-triggering; Fusion estimation; Multi-rate systems (MRSs); Stochastic nonlinearities (SNs); Tobit Kalman filter (TKF).