  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.
