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

页码：836 / 843

页数：8

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