A new adaptive track correlation method for multiple scenarios
Abstract The traditional track correlation methods have problems such as limitation of the application scenarios, unstable performances and poor practicalities. To solve those problems, a new adaptive track correlation method for multiple scenarios was proposed in this paper using the theories and m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12101 |
| _version_ | 1798002898053890048 |
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| author | Yaqi Cui Yu Liu Tiantian Tang Hongfeng Zhu |
| author_facet | Yaqi Cui Yu Liu Tiantian Tang Hongfeng Zhu |
| author_sort | Yaqi Cui |
| collection | DOAJ |
| description | Abstract The traditional track correlation methods have problems such as limitation of the application scenarios, unstable performances and poor practicalities. To solve those problems, a new adaptive track correlation method for multiple scenarios was proposed in this paper using the theories and methods from machine learning. Through interpreting and translating the track correlation problem in the field of information fusion into the classification recognition problem in the field of machine learning, the new method was derived based on deep convolutional neural networks. The association performance and adaptation capabilities of the proposed method had been validated by simulation experiments. The results show that the proposed method is better than the traditional methods with respect to association performance and adaptation capabilities, and can solve the track association problems for multiple scenarios, for example sensors have systematic errors and targets are dense or in formation. Thus, it can be predicted that the proposed method would have a well‐applied foreground. |
| first_indexed | 2024-04-11T11:59:28Z |
| format | Article |
| id | doaj.art-3eb229a93d44430bbfb56ecdebd940e1 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-04-11T11:59:28Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-3eb229a93d44430bbfb56ecdebd940e12022-12-22T04:24:53ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922021-09-011591112112410.1049/rsn2.12101A new adaptive track correlation method for multiple scenariosYaqi Cui0Yu Liu1Tiantian Tang2Hongfeng Zhu3Institute of Information Fusion Naval Aviation University Yantai Shandong ChinaInstitute of Information Fusion Naval Aviation University Yantai Shandong ChinaInstitute of Information Fusion Naval Aviation University Yantai Shandong ChinaInstitute of Information Fusion Naval Aviation University Yantai Shandong ChinaAbstract The traditional track correlation methods have problems such as limitation of the application scenarios, unstable performances and poor practicalities. To solve those problems, a new adaptive track correlation method for multiple scenarios was proposed in this paper using the theories and methods from machine learning. Through interpreting and translating the track correlation problem in the field of information fusion into the classification recognition problem in the field of machine learning, the new method was derived based on deep convolutional neural networks. The association performance and adaptation capabilities of the proposed method had been validated by simulation experiments. The results show that the proposed method is better than the traditional methods with respect to association performance and adaptation capabilities, and can solve the track association problems for multiple scenarios, for example sensors have systematic errors and targets are dense or in formation. Thus, it can be predicted that the proposed method would have a well‐applied foreground.https://doi.org/10.1049/rsn2.12101correlation methodssensor fusiontarget trackingconvolutional neural netsdeep learning (artificial intelligence) |
| spellingShingle | Yaqi Cui Yu Liu Tiantian Tang Hongfeng Zhu A new adaptive track correlation method for multiple scenarios IET Radar, Sonar & Navigation correlation methods sensor fusion target tracking convolutional neural nets deep learning (artificial intelligence) |
| title | A new adaptive track correlation method for multiple scenarios |
| title_full | A new adaptive track correlation method for multiple scenarios |
| title_fullStr | A new adaptive track correlation method for multiple scenarios |
| title_full_unstemmed | A new adaptive track correlation method for multiple scenarios |
| title_short | A new adaptive track correlation method for multiple scenarios |
| title_sort | new adaptive track correlation method for multiple scenarios |
| topic | correlation methods sensor fusion target tracking convolutional neural nets deep learning (artificial intelligence) |
| url | https://doi.org/10.1049/rsn2.12101 |
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