Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases
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,...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/294657 |
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| _version_ | 1832556934100680704 |
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| author | Hongyan Zhu Suying Han |
| author_facet | Hongyan Zhu Suying Han |
| author_sort | Hongyan Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0b0c9a7ff9684843baef61928963f258 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-0b0c9a7ff9684843baef61928963f2582025-02-03T05:43:57ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/294657294657Track-to-Track Association Based on Structural Similarity in the Presence of Sensor BiasesHongyan Zhu0Suying Han1School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThe 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.http://dx.doi.org/10.1155/2014/294657 |
| spellingShingle | Hongyan Zhu Suying Han Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases Journal of Applied Mathematics |
| title | Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases |
| title_full | Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases |
| title_fullStr | Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases |
| title_full_unstemmed | Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases |
| title_short | Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases |
| title_sort | track to track association based on structural similarity in the presence of sensor biases |
| url | http://dx.doi.org/10.1155/2014/294657 |
| work_keys_str_mv | AT hongyanzhu tracktotrackassociationbasedonstructuralsimilarityinthepresenceofsensorbiases AT suyinghan tracktotrackassociationbasedonstructuralsimilarityinthepresenceofsensorbiases |