Multiple Target Tracking Based on Sets of Trajectories

被引：64

作者：

Garcia-Fernandez, Angel F. ^{[1
,2
]}

Svensson, Lennart ^{[3
]}

Morelande, Mark R. ^{[4
]}

机构：

[1] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England

[2] Univ Antonio de Nebrija, ARIES Res Ctr, Madrid, Spain

[3] Chalmers Univ Technol, Dept Elect Engn, SE-41296 Gothenburg, Sweden

[4] Natl Australia Bank, Melbourne, Vic 3000, Australia

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2020年 / 56卷 / 03期

关键词：

MONTE-CARLO METHODS; RANDOM FINITE SETS; MULTITARGET TRACKING; PARTICLE FILTER; DERIVATION; ALGORITHM;

D O I：

10.1109/TAES.2019.2921210

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to TT should characterize the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multiobject density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.

引用

页码：1685 / 1707

页数：23

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