We present an exploration of collection geometries for producing three-dimensionally (3D) focused synthetic aperture radar (SAR) derived point clouds. We consider collection geometries that can be produced by a series of continuous curves such as multiple flight paths of a fixed wing aircraft or multiple passes of a satellite orbiting the earth. As part of our analysis, we use sparse methods to reconstruct undersampled radar data. We use back-projection to focus the radar data into the spatial domain, onto a uniform volumetric grid. Additionally, we use a 3D resonance finding algorithm to extract scattering centers from volumetric radar data to produce 3D point clouds. Our analysis is based upon synthetic radar data produced using the parameters derived from our laboratory’s in-door turntable inverse synthetic radar aperture (ISAR) system. A key point of our analysis is to determine how many repeat passes are required to achieve a given fidelity of an object’s 3D representation. Analysis will include a comparison with interferometric methods, particularly with regard to the fidelity and the point cloud density. We use a digital model of a civilian pickup truck that has been validated for use in synthetic prediction, both as a full-size model in outdoor collects as well as a reduced scale model measured indoors in our lab. Future research directions are also discussed.

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Paul Sotirelis and Sean Gilmore, "3D SAR image reconstruction of ground vehicles using sparse multiple flight path data," Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 1252005 (Presented at SPIE Defense + Commercial Sensing: May 02, 2023; Published: 13 June 2023); https://doi.org/10.1117/12.2663642.