Maximum‐correntropy‐based Kalman filtering for time‐varying systems with randomly occurring uncertainties: An event‐triggered approach. (20th December 2020)

• Record Type: Journal Article
• Authors: Su, Tingli; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 5(2021)
• Page Start: 1582
• Page End: 1599
• Publication Date: 2020-12-20
• Subjects: event‐triggered mechanism -- Kalman filter -- maximum correntropy criterion -- non‐Gaussian noise -- randomly occurring uncertainties; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5368
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: In this article, the maximum‐correntropy‐based Kalman filtering problem is investigated for a class of linear time‐varying systems in the presence of non‐Gaussian noises and randomly occurring uncertainties (ROUs). The random nature of the parameter uncertainties is characterized by a stochastic variable conforming to the Bernoulli distribution. In order to avoid unnecessary data transmission and reduce consumption of limited communication resource, the event‐triggered mechanism (ETM) is introduced in the sensor‐to‐filter channel to decide whether the data should be transmitted or not. A novel performance index is first proposed to reflect the joint effects from the non‐Gaussian noises, the ETM as well as the ROUs. Under the proposed performance index, an event‐based Kalman filter is then constructed whose gain is calculated based on the maximum correntropy criterion. Finally, the effectiveness of the proposed filtering scheme is verified via a practical target tracking example.