@inproceedings{7ecd9fa512b84ee6ad341cb37992a35e,
title = "Information Gain-weighted Multi-sensor Arithmetic Average Fusion Kalman Filtering",
abstract = "This paper presents a novel multi-sensor Kalman filter (KF) based on the arithmetic average (AA) fusion method. In this approach, the fusing weights are designed according to the online Kalman gain matrix obtained from each local filter. Both the standard KF and the unscented KF (UKF) are applied to linear and nonlinear state space models, respectively. Simulation results demonstrate the superior target tracking performance of our approach compared to the recently proposed suboptimal AA fusion method using the Kullback-Leibler divergence (KLD) in both linear and nonlinear scenarios.",
keywords = "Arithmetic average, Kalman filter, multi-sensor fusion, target tracking",
author = "Hongfei Li and Guchong Li and Tiancheng Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023 ; Conference date: 27-11-2023 Through 29-11-2023",
year = "2023",
doi = "10.1109/ICCAIS59597.2023.10382245",
language = "英语",
series = "Proceedings - 12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "230--235",
editor = "Tung, \{Truong Xuan\} and Tan, \{Tran Cong\} and Tinh, \{Cao Huu\}",
booktitle = "Proceedings - 12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023",
}