Multistatic-polarimetric SAR for sparsely sampled 3D imaging

Abstract

Synthetic Aperture Radar (SAR) provides fine-resolution imagery in all weather conditions during both day and night. SAR can provide 3D imaging capability, enhancing image interpretability and target recognition. Producing sufficient scatterer resolvability and coverage in the vertical imaging direction can be challenging due to the often-impractical data collection requirements of 3D SAR imaging. This thesis presents a novel algorithm for exploiting multistatic sensing opportunities to reduce the sampling requirements of forming high-quality 3D SAR imagery, which is named the SSARVI algorithm. An extension of the SSARVI algorithm to polarimetric SAR analyses is also presented, named the PolSSARVI algorithm. The development of both algorithms addresses limitations in the literature of applying sparse 3D SAR approaches to multistatic SAR geometries. Both simulated and experimental data are used throughout this thesis to validate and optimise the performance of the algorithms developed. Their performance is compared to back projection (BPA)-formed imagery at Nyquist sampling in elevation. The results are presented across three distinct publication chapters.