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.

翻译：统计跟踪滤波器的良好跟踪性能依赖于精确的目标测量与不确定性估计。本研究针对地面动目标指示雷达的距离-多普勒图像，提出了用于目标检测与不确定性估计的新型机器学习模型。我们证明，通过使用这些模型的输出，能够显著提升多假设跟踪器在复杂多目标空对地跟踪场景中的性能。