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

• URL: http://arxiv.org/abs/2407.03140v1
• Date: Wed, 3 Jul 2024 14:20:24 GMT
• Title: Machine Learning Models for Improved Tracking from Range-Doppler Map Images
• Authors: Elizabeth Hou, Ross Greenwood, Piyush Kumar,
• Abstract summary: 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.
• Score: 1.3654846342364306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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