Deep Learning Radio Frequency Signal Classification with Hybrid Images

In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. This work focuses on the different pre-processing steps that can be used on the input training data, and tests the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, in this work a hybrid image is proposed that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. The initial results point out limitations to classical pre-processing approaches while also showing that it’s possible to build a classifier that can leverage the strengths of multiple signal representations.