@inproceedings{d79c4f6befc246f78360089ede5f1025,
title = "Kullback-leibler averaging for multitarget density fusion",
abstract = "This paper addresses the linear and log-linear fusion approaches to multitarget density fusion which yield arithmetic average (AA) and geometric average (GA), respectively. We reaffirm Abbas{\textquoteright}s finding in 2009 that both AA and GA can be related to the minimization of the Kullback-Leibler divergence (KLD) between the fusing densities and the fused result, which differ from each other in the reference used to measure the KLD: the AA uses the fusing densities while the GA uses the fused density. We derive the explicit AA expressions for fusing some known multitarget densities and discuss the implementation issues. The results serve as the theoretical basis for designing distributed random finite set filters for distributed multitarget tracking.",
keywords = "Arithmetic average, Average consensus, Linear fusion, Random finite set, Sensor network, Target tracking",
author = "Kai Da and Tiancheng Li and Yongfeng Zhu and Hongqi Fan and Qiang Fu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 16th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2019 ; Conference date: 26-06-2019 Through 28-06-2019",
year = "2020",
doi = "10.1007/978-3-030-23887-2_29",
language = "英语",
isbn = "9783030238865",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "253--261",
editor = "Francisco Herrera and Kenji Matsui and Sara Rodr{\'i}guez-Gonz{\'a}lez",
booktitle = "Distributed Computing and Artificial Intelligence, 16th International Conference, 2019",
}