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

• Published in: ISA transactions Vol. 110; pp. 28 - 38
• Main Authors: Huang, Cong, Mei, Peng, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2021
• Subjects: Censored observations (COs); Event-triggering; Fusion estimation; Multi-rate systems (MRSs); Stochastic nonlinearities (SNs); Tobit Kalman filter (TKF); Fusion estimation; Event-triggering; Tobit Kalman filter (TKF); Censored observations (COs); Multi-rate systems (MRSs); Stochastic nonlinearities (SNs)
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2020.10.038

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. •The phenomena of the parameter uncertainties, stochastic nonlinearities and censored measurements are considered in a unified form.•A novel augmentation technology is utilized to transform the MRS into single-rate system.•Each local filter parameters are derived by minimized the each upper bound of local filtering error covariance at each sampling instant.•A CI fusion scheme is proposed and fusion estimation is derived with the help of the local estimates.