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

2024/07/03 by Elizabeth Hou, Hou, Elizabeth, Ross Greenwood +3
Computer Science · #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.2407.03140

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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