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AI-aided Supar Super-Resolution for Sensing Arrays

This research initiative aims at achieving unprecedented resolution using miniature size sensor arrays. In electromagnetic and acoustic sensing, physical size of sensor arrays limits their angular resolution. In this research, different frequency signals are used to achieve Synthetic UPsampling ARray (SUPAR) super-resolution, which allows to improve angular resolution in multitude. Artificial intelligence (AI) tools will be employed for real time probing design and imaging, paving the way for commercial adoption. The output of this research is expected to make significant impacts for 1) sensing using mobile platforms (e.g. vehicles) where physical space is a precious resource; 2) see-through imaging where low frequency signals are typically employed for signal penetration capability but could lead to low resolution; 3) high resolution functional imaging where scenes change rapidly.

Led by Dr Wei Dai.


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