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2024, Volume 25, Issue 2

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Frontiers of Information Technology & Electronic Engineering >> 2024, Volume 25, Issue 2 doi: 10.1631/FITEE.2300508

Outlier-resistant distributed fusion filtering for nonlinear discrete-time singular systems under a dynamic event-triggered scheme

Affiliation(s): Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, China; Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems, Harbin University of Science and Technology, Harbin 150080, China; School of Automation, Harbin University of Science and Technology, Harbin 150080, China; School of Mathematics and Physics, Anhui Polytechnic University, Wuhu 241000, China; School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314003, China; Tangshan Research Institute, Beijing Institute of Technology, Tangshan 063099, China; less

Received: 2023-07-28 Accepted: 2024-02-23 Available online: 2024-02-23
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Abstract

This paper investigates the problem of outlier-resistant (DFF) for a class of (MSNSSs) under a (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization, a DETS is adopted to regulate the frequency of data transmission. For the addressed MSNSSs, our purpose is to construct the local under the effects of the measurement outliers and the DETS; the local upper bound (UB) on the filtering error covariance (FEC) is derived by solving the difference equations and minimized by designing proper filter gains. Furthermore, according to the local filters and their UBs, a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule. As such, the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers, thereby improving the estimation performance. Moreover, the of the filtering error is discussed and a corresponding sufficient condition is presented. Finally, the validity of the developed algorithm is checked using a simulation example.

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