Robust Fusion for Multisensor Multiobject Tracking

被引：115

作者：

Fantacci, Claudio ^{[1
]}

Vo, Ba-Ngu ^{[2
]}

Vo, Ba-Tuong ^{[2
]}

Battistelli, Giorgio ^{[3
]}

Chisci, Luigi ^{[3
]}

机构：

[1] Ist Italiano Tecnol, I-16163 Genoa, Italy

[2] Curtin Univ, Dept Elect & Comp Engn, Bentley, WA 6102, Australia

[3] Univ Firenze, Dipartimento Ingn Informaz, I-50139 Florence, Italy

来源：

IEEE SIGNAL PROCESSING LETTERS | 2018年 / 25卷 / 05期

基金：

澳大利亚研究理事会;

关键词：

Finite set statistics (FISST); generalized labeled multi-Bernoulli (GLMB); labeled multi-Bernoulli (LMB); marginalized delta-GLMB (M delta-GLMB); multiobject densities; random finite set (RFS); RANDOM FINITE SETS; MULTITARGET TRACKING; CONSENSUS; FILTER; DENSITIES;

D O I：

10.1109/LSP.2018.2811750

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter proposes analytical expressions for the fusion of certain classes of labeled multiobject densities via Kullback-Leibler averaging. Specifically, we provide analytical fusion rules for the labeled multi-Bernoulli and marginalized delta-generalized labeled multi-Bernoulli families of labeled multiobject densities. Information fusion via Kullback-Leibler averaging ensures immunity to double counting of information and is essential to the development of effective multiagent multiobject estimation.

引用

页码：640 / 644

页数：5

共 27 条

[11] Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability [J].