Fast filtering of mobile signals in radar warning receiver systems using machine learning

Abstract: The radio frequency spectrum is becoming increasingly crowded and research efforts are being made both from the side of communication and from radar to allow for sharing of the radio frequency spectrum. In this thesis, suitable methods for classifying incoming signals as either communication signals or radar signals using machine learning are evaluated, with the purpose of filtering communication signals in radar warning receiver systems. To this end, a dataset of simulated communication and radar signals is generated for evaluation. The methods are evaluated in terms of both accuracy and computational complexity since both of these aspects are critical in a radar warning receiver setting. The results show that a deep learning model can be designed to outperform expert feature-based models in terms of accuracy, as has previously been confirmed in other fields. In terms of computational complexity, however, they are vastly outperformed by a model based on ensemble decision trees. As such, a deep learning model may be too complex for the task of filtering communication signals from radar signals in a radar warning receiver setting. The classification accuracy needs to be weighed against the model size and classification time. Future work should focus on optimizing the feature extraction implementation for a more fair classification time comparison, as well as evaluating the models on recorded data.