Guest Editorial for the TAES Special Section on Deep Learning for Radar Applications.

It has been roughly a decade since the first papers using deep neural networks (DNNs) for radar applications were published. Deep learning has revolutionized almost every technical area, from computer vision and natural language processing to health, finance, and biology—any field where data can be analyzed to provide insight. However, in radar applications, deep learning faces unique challenges due to the phenomenology of radio frequency (RF) propagation that creates essential differences in the data itself and impacts the design of DNNs for radar signal analysis [1], [2], [3].