FMCW Radar deterministic Augmentation Applied to Deep Learning Networks......... -Part 1

Deep neural networks (DNNs) have become a relevant subject in the classification of radio frequency signals and remote sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN training and the effort that making experimental measurements requires. Hence, the quality and quantity of data used for the training and testing of models are crucial for effective classifier development. The training dataset should cover a wide range of cases that synthesize the actual scenarios being classified.

The effectiveness of the proposed methodology is proven by considering a deterministic model: by comparing the classification accuracy using a convolutional neural network (CNN) trained using the synthetic dataset, and by using a typical signal processing data augmentation method on a limited measured dataset.