  Unlike areas such as computer vision and speech recognition where
convolutional and recurrent neural networks-based approaches have proven
effective to the nature of the respective areas of application, deep learning
(DL) still lacks a general approach suitable for the unique nature and
challenges of RF systems such as radar, signals intelligence, electronic
warfare, and communications. Existing approaches face problems in robustness,
consistency, efficiency, repeatability and scalability. One of the main
challenges in RF sensing such as radar target identification is the difficulty
and cost of obtaining data. Hundreds to thousands of samples per class are
typically used when training for classifying signals into 2 to 12 classes with
reported accuracy ranging from 87% to 99%, where accuracy generally decreases
with more classes added. In this paper, we present a new DL approach based on
multistage training and demonstrate it on RF sensing signal classification. We
consistently achieve over 99% accuracy for up to 17 diverse classes using only
11 samples per class for training, yielding up to 35% improvement in accuracy
over standard DL approaches.
