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
收藏
页码：6328 / 6332
页数：5
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