Radar-Cued Camera Data Collection System

The rapid increase in the availability and sophistication of UAS (Unmanned Aircraft Systems) represents a significant challenge, as their capabilities progress faster than the ability to assess and mitigate the threat posed by nefarious small UAS. Counter Unmanned Aircraft Systems (C-UAS) is a collection of technologies to detect, track and mitigate UAS systems. Echodyne's radar technology is a leader in the active RF detection and tracking of UAS and, when combined with an EO/IR camera for visual target confirmation, provides a very effective system solution. Independently classifying a UAS target with these two modalities and fusing the result would yield improved classification precision and accuracy. Cueing pan/tilt/zoom cameras based on a 3D radar track is a fairly established technology. Using an Echodyne radar with tracked target classification output and applying advanced machine learning techniques, this student teamworked to leverage an available camera cueing solution and demonstrate both camera-based target classification and the fused combination with radar data. The outcomes this student team worked toward include: - Develop a proof of concept system that takes radar tracks, cues an EO/IR camera and provides real-time UAS image classification and serves as a reference design for future product developments - Develop ML/AI training models and inference algorithms for UAS image classification - Demonstrate the benefit of multi-modal (radar + optical/IR) sensor UAS classification with precision/accuracy metrics - Document how to extend the training dataset and retrain any models, possibly extending to other detection classes (pedestrians, vehicles, planes, etc.) - Document implemented system, lessons learned and recommended next steps

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