Machine Learning Models for Improved Tracking from Range-Doppler Map Images
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916310675357696 |
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| author | Hou, Elizabeth Greenwood, Ross Kumar, Piyush |
| author_facet | Hou, Elizabeth Greenwood, Ross Kumar, Piyush |
| contents | Statistical tracking filters depend on accurate target measurements and uncertainty estimates for good tracking performance. In this work, we propose novel machine learning models for target detection and uncertainty estimation in range-Doppler map (RDM) images for Ground Moving Target Indicator (GMTI) radars. We show that by using the outputs of these models, we can significantly improve the performance of a multiple hypothesis tracker for complex multi-target air-to-ground tracking scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Models for Improved Tracking from Range-Doppler Map Images Hou, Elizabeth Greenwood, Ross Kumar, Piyush Computer Vision and Pattern Recognition Statistical tracking filters depend on accurate target measurements and uncertainty estimates for good tracking performance. In this work, we propose novel machine learning models for target detection and uncertainty estimation in range-Doppler map (RDM) images for Ground Moving Target Indicator (GMTI) radars. We show that by using the outputs of these models, we can significantly improve the performance of a multiple hypothesis tracker for complex multi-target air-to-ground tracking scenarios. |
| title | Machine Learning Models for Improved Tracking from Range-Doppler Map Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.03140 |