In this work we describe a methodology to process the data acquired by a conventional mm-wave automotive Radar to form an image of static targets ahead of the ego-vehicle at a spatial resolution several times finer than allowed by the physical size of the Radar array. High-resolution imaging is achieved by applying a processing scheme typical of Synthetic Aperture Radars (SAR), that is by exploiting the forward motion of the egovehicle to synthesize an aperture as long as several centimeters. Contrary to many works in literature, where SAR imaging is achieved at the expense of a significant increase of computational costs, we propose here a Quick and Dirty (Q&D) approach that can be implemented at computational costs comparable with those of standard automotive Radar imaging. The effectiveness of this approach is demonstrated on the basis of real data collected during an open road acquisition campaign, by comparing Q&D SAR images with accurate SAR images produced by Time Domain Back Projection. Finally, it is shown that the algorithm can easily be tuned to produce SAR imaging of moving targets.

A Quick and Dirty processor for automotive forward SAR imaging

Tebaldini, Stefano;Rizzi, Marco;Manzoni, Marco;Guarnieri, Andrea Monti;Prati, Claudio;Tagliaferri, Dario;Nicoli, Monica;Spagnolini, Umberto;
2022-01-01

Abstract

In this work we describe a methodology to process the data acquired by a conventional mm-wave automotive Radar to form an image of static targets ahead of the ego-vehicle at a spatial resolution several times finer than allowed by the physical size of the Radar array. High-resolution imaging is achieved by applying a processing scheme typical of Synthetic Aperture Radars (SAR), that is by exploiting the forward motion of the egovehicle to synthesize an aperture as long as several centimeters. Contrary to many works in literature, where SAR imaging is achieved at the expense of a significant increase of computational costs, we propose here a Quick and Dirty (Q&D) approach that can be implemented at computational costs comparable with those of standard automotive Radar imaging. The effectiveness of this approach is demonstrated on the basis of real data collected during an open road acquisition campaign, by comparing Q&D SAR images with accurate SAR images produced by Time Domain Back Projection. Finally, it is shown that the algorithm can easily be tuned to produce SAR imaging of moving targets.
2022
2022 IEEE Radar Conference (RadarConf22)
978-1-7281-5368-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1231683
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