Machine Learning Models for Improved Tracking from Range-Doppler Map Images

Elizabeth Hou, Ross Greenwood, Piyush Kumar
7/3/2024
cs.CV

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Abstract

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.

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Cite this paper

@article{hou2024machine,
  title  = {Machine Learning Models for Improved Tracking from Range-Doppler Map Images},
  author = {Elizabeth Hou and Ross Greenwood and Piyush Kumar},
  year   = {2024},
  eprint = {2407.03140},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.03140v1}
}

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