Squinted SAR focusing for improving automatic radar sounder data analysis and enhancement

Radar sounder (RS) instruments are providing a huge amount of subsurface data. In order to support the study of this data, several automatic methods have been recently proposed. So far, the development of such methods mostly focused on publicly available radargrams (standard products), which are generated from raw data in order to obtain high visual quality images. The possibility to exploit raw processing to derive additional information for automatic analyses has not yet been considered. In order to fill this gap, in this paper, we show that by properly tuning raw signal processing it is possible to automatically obtain additional a priori information on subsurface targets. Such information can be used to potentially improve the results of further automatic analyses and/or address problems that cannot be easily solved automatically using only standard products. In particular, we propose four measurements obtained using squinted synthetic aperture radar focusing that provide useful physical information about subsurface features. Moreover, in order to prove the effectiveness of the proposed approach, a novel preprocessing method for automatic layer detection techniques based on the concepts developed in this paper is presented and validated. All the examples reported in the paper use real planetary RS data acquired by the SHAllow RADar instrument on Mars.