New models and super resolution techniques for short range radar: Theory and experiments
Short range millimeter (mm) wave radar imaging has significant potential for emerging applications such as handheld based gesture recognition and vehicular situational awareness. In this paper, we develop a theoretical framework for an array of monostatic elements for this purpose. We show that we must account for form factor and complexity constraints in a manner that is quite different from that of conventional radar, and introduce new models and algorithms validated by experimental results. Our main results include (a) identification of the number of degrees of freedom as a function of scene and array geometry, (b) demonstrating that grating lobes seen for the conventional point scatterer model when we employ sparse arrays can be eliminated by using spatial aggregation, using patch based scatterer models as our dictionary, (c) optimization of patch size based on estimation theoretic bounds, (d) super resolution using a recently developed algorithm combining Newton methods with greedy pursuit.
2016 Information Theory and Applications Workshop (ITA)