Machine Learning Models for Improved Tracking from Range-Doppler Map Images
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Abstract (arXiv)
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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References · 3
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
- 2019Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restorationcited by 2
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