Track‐to‐Track Association Based on Structural Similarity in the Presence of Sensor Biases

Hongyan Zhu and Suying Han

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

Abstract: 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.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2014/294657

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:294657

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().