Tensor Tomographic Imaging for Foliage Penetration (FOPEN)

The main aim of this PhD is to develop the tools that are fundamental in resolving the challenges faced by Radar Imaging, especially in a forestry environment. For instance, how would we alleviate the non-linear effects of Electromagnetic wave scattering due to objects of interests in Synthetic Aperture Radar (SAR) images? Developments in more accurate sensor systems coupled with the increase in computational power for portable devices give rise to an explosion in the hardware to capture rich datasets.

Low Frequency Electromagnetic waves penetrate foliage better, but result in lower resolution images using known image formation algorithms. Furthermore, the received multi-path/bounce of Electromagnetic waves scattered by anisotropic objects adds complexity in imaging areas of dense vegetation. It is envisaged that a rich data structure of multi-polarisation, multi-frequency, multi-look and multi-static geometry will ease in the process of acquiring a volumetric image of the region of interest with an enhanced description of the scene, i.e. tensor tomography. Due to the effects of Electromagnetic waves in a foliage environment, the retrieval of a high resolution image is classed as a nonlinear Inverse Problem, specifically as Inverse Boundary Value Problems (BVPs) for Maxwell's equations.