Volumetric interferometry for sparse 3D synthetic aperture radar with bistatic geometries

Abstract

Synthetic Aperture Radar (SAR) renderings in 3D provide additional target information when compared to 2D by separating out features overlaid in height. However, the required 2D SAR aperture, when Nyquist sampled, necessitates large scanning times that would be impractical for most realistic collections. This research has developed a novel volumetric approach to sparse aperture 3D SAR imaging, which is applicable to bistatic SAR near-field geometries, a generalization of far-field cases. This approach is first demonstrated in simulation and then applied to a measured scene containing a model vehicle target, producing sub-Nyquist sampled 3D SAR renderings.

Laboratory multistatic 3D SAR with polarimetry and sparse aperture sampling

“…A multistatic SAR geometries extension of the SSARVI approach for generating sub-Nyquist SAR aperture 3D pointcloud renderings [19] was presented. With simulations, it was shown that excessive clustering of trajectories can reduce the sidelobe performance substantially, essentially due to multiple trajectories behaving as a single trajectory.…”

“…It is a generalisation of the SPCGS algorithm, in this case applicable to multistatic and SAR near-field imaged scenes. Previously the performance of the algorithm was demonstrated for monostatic and bistatic scenarios for a single polarisation channel [19], whereas here it is demonstrated on multistatic collections. It exploits the benefits of volumetric processing, allowing laid over scatterers to be processed and distinguished in a natural manner [20].…”

“…The R sidelobe height gives rise to false detections in the SSARVI algorithm, which can be influenced by the number of passes and the unevenness of the spacing between them. It has been found that generally, but only to an extent, greater unevenness in trajectory height sampling leads to greater sidelobe suppression [19]. However, it is thought that when the unevenness is taken to the extreme within a fixed extent, passes can cluster together, effectively reducing the trajectory sampling, which is prejudicial to R sidelobe suppression.…”

“…To explore the performance of the SSARVI algorithm for multistatic SAR collections, simulations of a single point scatterer were conducted and are described in this section. Previously it was found that randomized spacing trajectory distributions in height with a normalised trajectory spacing standard deviation (NSTD) of approximately 0.6 gave the best peak to sidelobe performance [19]. However, for higher NSTD values the peak to sidelobe performance was seen to drop again.…”

Laboratory Multistatic Polarimetric Sparse Aperture 3d Sar Investigation

“…We have generalized the SPCGS algorithm into a novel Sparse SAR Volumetric Interferometry (SSARVI) processing-based technique which produces 3D SAR imagery from sparsely sampled SAR aperture data, for bistatic, multistatic and SAR near-field target scenarios 8 . The motivation behind the development was to provide an image formation algorithm suitable for envisaged multistatic SAR collections from satellite constellations and UAV-SAR drone swarms 9,10,11 .…”