Kullback-Leibler Averaging for Multitarget Density Fusion

被引:35
作者
Da, Kai [1 ]
Li, Tiancheng [2 ]
Zhu, Yongfeng [1 ]
Fan, Hongqi [1 ]
Fu, Qiang [1 ]
机构
[1] Natl Univ Def Technol, Coll Elect Sci, Natl Key Lab Sci & Technol ATR, Changsha 410073, Hunan, Peoples R China
[2] Northwestern Polytech Univ, Sch Automat, Key Lab Informat Fus Technol, Minist Educ, Xian 710072, Shaanxi, Peoples R China
来源
DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 16TH INTERNATIONAL CONFERENCE | 2020年 / 1003卷
关键词
Average consensus; Arithmetic average; Linear fusion; Random finite set; Sensor network; Target tracking; CONSENSUS;
D O I
10.1007/978-3-030-23887-2_29
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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'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.
引用
收藏
页码:253 / 261
页数:9
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