Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-25
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The paper addresses the problem of track-to-track association in the presence of sensor biases.
In some challenging scenarios, it may be infeasible to implement bias estimation and compensation in time due to the computational intractability or weak observability about sensor biases.
In this paper, we introduce the structural feature for each local track, which describes the spatial relationship with its neighboring targets.
Although the absolute coordinates of local tracks from the same target are severely different in the presence of sensor biases, their structural features may be similar.
As a result, instead of using the absolute kinematic states only, we employee the structural similarity to define the association cost.
When there are missed detections, the structural similarity between local tracks is evaluated by solving another 2D assignment subproblem.
Simulation results demonstrated the power of the proposed approach.
American Psychological Association (APA)
Zhu, Hongyan& Han, Suying. 2014. Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-461154
Modern Language Association (MLA)
Zhu, Hongyan& Han, Suying. Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases. Journal of Applied Mathematics No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-461154
American Medical Association (AMA)
Zhu, Hongyan& Han, Suying. Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-461154
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-461154