DRIFTERS: Deep Radar Interpretation For Tracking and Enhancement of Raw Signal

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Deep learning has been highly successful in fields such as computer vision and speech recognition, due to its capacity to learn representations able to process raw signals to achieve high level tasks. In this research project, we will develop approaches based on deep learning for airborne maritime radar signal processing, where the signal is relatively unstructured and severely noisy. More specifically, we will assess approaches to 1) suppress sea clutter and detect targets in static signal; 2) detect targets from sequential radar signal; and 3) track targets from sequential signals. Moreover, deep learning requires big datasets covering a variety of contexts to attain good results, while the databases publicly available are generally limited to specific environments. Thus, we will also explore deep learning-based approaches to enhance the data obtained from simulation models, in order to augment the dataset and cover more situations with signals having realistic characteristics (e.g., sophisticated noise components). This has the potential of leading to the discovery of better ways to process radar signals and as such improve the performance of sensing devices based on this technology, with a possible positive impact on coastal surveillance for increasing national security and safety in maritime environments.