Target Tracking Formulation of the SVSF as a Probabilistic Data Association Algorithm

被引：0

作者：

Attari, Mina ^{[1
]}

Gadsden, S. Andrew ^{[1
]}

Habibi, Saeid R. ^{[1
]}

机构：

[1] McMaster Univ, Dept Mech Engn, Hamilton, ON L8S 4L7, Canada

来源：

2013 AMERICAN CONTROL CONFERENCE (ACC) | 2013年

关键词：

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：

Target tracking algorithms are important for a number of applications, including: physics, air traffic control, ground vehicle monitoring, and processing medical images. The probabilistic data association algorithm, in conjunction with the Kalman filter (KF), is one of the most popular and well-studied strategies. The relatively new smooth variable structure filter (SVSF) offers a robust and stable estimation strategy under the presence of modeling errors, unlike the KF method. The purpose of this paper is to introduce and formulate the SVSF-PDA, which can be used for target tracking. A simple example is used to compare the estimation results of the popular KF-PDA with the new SVSF-PDA.

引用

页码：6328 / 6332

页数：5

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