A Comparative Analysis of QADA-KF with JPDAF for Multitarget Tracking in Clutter

被引：0
|
作者：
Dezert, Jean [1 ]
Tchamova, Albena [2 ]
Konstantinova, Pavlina [3 ]
Blasch, Erik [4 ]
机构：
[1] Off Natl Etud & Rech Aerosp, Palaiseau, France
[2] BAS, Inst I&C Tech, Sofia, Bulgaria
[3] Eur Polytech Univ, Pernik, Bulgaria
[4] AFRL, Rome, GA USA
来源：
2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2017年
关键词：
Data association; JPDAF; Belief Functions; QADA; PCR6; rule; Multitarget Tracking;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a comparative analysis of performances of two types of multi-target tracking algorithms: 1) the Joint Probabilistic Data Association Filter (JPDAF), and 2) classical Kalman Filter based algorithms for multi-target tracking improved with Quality Assessment of Data Association (QADA) method using optimal data association. The evaluation is based on Monte Carlo simulations for difficult maneuvering multiple-target tracking (MTT) problems in clutter.
引用
收藏
页码：836 / 843
页数：8
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