Maximum‐correntropy‐based Kalman filtering for time‐varying systems with randomly occurring uncertainties: An event‐triggered approach

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.