Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The random matrix (RM) method is widely applied for group target tracking.
The assumption that the group extension keeps invariant in conventional RM method is not yet valid, as the orientation of the group varies rapidly while it is maneuvering; thus, a new approach with group extension predicted is derived here.
To match the group maneuvering, a best model augmentation (BMA) method is introduced.
The existing BMA method uses a fixed basic model set, which may lead to a poor performance when it could not ensure basic coverage of true motion modes.
Here, a maneuvering group target tracking algorithm is proposed, where the group extension prediction and the BMA adaption are exploited.
The performance of the proposed algorithm will be illustrated by simulation.
American Psychological Association (APA)
Gan, Linhai& Wang, Gang. 2017. Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1190786
Modern Language Association (MLA)
Gan, Linhai& Wang, Gang. Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted. Mathematical Problems in Engineering No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1190786
American Medical Association (AMA)
Gan, Linhai& Wang, Gang. Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1190786
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190786