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Tensor Tomographic Imaging for Foliage Penetration: CASE project with DSTL


Department of Mathematics

Manchester United Kingdom Applied Mathematics Electrical Engineering Electromagnetism Optical Physics Software Engineering Statistics

About the Project

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.

In summary, the task is twofold:

1.  Develop an Electromagnetic propagation solver for a forestry environment; and

2.  Develop reconstruction algorithms that exploit the rich-nature of SAR datasets by processing them into meaningful images.

The Defence Science and Technology Laboratory (DSTL), an executive agency for the Ministry of Defence (MoD) are seeking to drive game-changing and novel Science & Technology (S&T) within the Radar field, with a focus on developing innovative technologies and techniques that could deliver operational advantage and freedom of action in the future. One particular area of interest is volumetric multi-static SAR imagery to conduct surveillance and derive intelligence of the ground under tree canopies. 

Academic background of candidates

Applicants are expected to hold, or about to obtain, a first class undergraduate degree (or equivalent) in Mathematics/Physics/Computer

Science/Electronic Engineering. A Masters degree in a relevant subject is desirable. Furthermore, applicants are particularly encouraged to apply who have:

1. Enthusiasm for good programming (e.g. C/C++, MATLAB/ Python) with a willingness to learn High Performance Computing techniques

(e.g. MPI and/or OpenMP);

2. Knowledge of Electromagnetic scattering or a willingness to learn;

3. Interest in numerical methods such as Finite Element Methods (FEM), Boundary Element Methods (BEM) and/or Finite Difference Time Domain solvers for Electromagnetic scattering; and

4. A general background to Inverse Problems. 

Contact for further Information

Professor Bill Lionheart

Email –

Webpage - https://www.research.manchester.ac.uk/portal/bill.lionheart.html


Funding Notes

This is a 4 year EPSRC iCASE studentship with The Defence Science and Technology Laboratory (DSTL), an executive agency for the Ministry of Defence (MoD). The funding will cover fees and stipend.

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