Unsupervised and interpretable track‐to‐track association based on homography estimation of radar bias
Abstract Track‐to‐track association methods based on machine learning and deep learning have greatly improved the association results, but the scope of application is limited by the poor interpretability and manual association labelling. To enhance the interpretability of the neural networks, enhanc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12483 |