Deep Learning Radio Frequency Signal Classification with Hybrid Images
This work addresses signal classification for RF applications, but it is incremental as it builds on existing deep learning methods by introducing a new pre-processing step.
The paper tackled the problem of classifying Radio Frequency signals by proposing a hybrid image approach that combines time and frequency domain information, treating it as a computer vision task, and found that this method can leverage multiple signal representations while highlighting limitations in classical pre-processing.
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. In this work, we focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, we propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. Our 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.