Electromagnetic (EM) imaging schemes are inherently nonlinear and illposed. Albeit there exist remedies to these fundamental problems, more efficient solutions are still being sought. To this end, in this thesis, the nonlinearity is tackled in corporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete nonlinear iterative Landweber schemes) that can account for weak to strong scattering problems. The illposedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint. More specifically, four novel in verse scattering schemes are formulated and implemented. (i) A greedy algorithm is used together with a simple artificial neural network (ANN) for efficient and accu rate EM imaging of weak scatterers. The ANN is used to predict the sparsity level of the investigation domain which is then used as the L0  constraint parameter for the greedy algorithm. (ii) An inexact Newton scheme that enforces the sparsity con straint on the derivative of the unknown material properties (not necessarily sparse) is proposed. The inverse scattering problem is formulated as a nonlinear function of the derivative of the material properties. This approach results in significant spar sification where any sparsity regularization method could be efficiently applied. (iii) A sparsity regularized nonlinear contrast source (CS) framework is developed to di rectly solve the nonlinear minimization problem using Landweber iterations where the convergence is accelerated using a selfadaptive projected accelerated steepest
descent algorithm. (iv) A 2.5D finite difference frequency domain (FDFD) based in
verse scattering scheme is developed for imaging scatterers embedded in lossy and inhomogeneous media. The FDFD based inversion algorithm does not require the Greenâ€™s function of the background medium and appears a promising technique for biomedical and subsurface imaging with a reasonable computational time.
Numerical experiments, which are carried out using synthetically generated mea surements, show that the images recovered by these sparsityregularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.
Date of Award  Apr 2020 

Original language  English (US) 

Awarding Institution   Computer, Electrical and Mathematical Science and Engineering


Supervisor  Hakan Bagci (Supervisor) 

 Electromagnetic inverse scattering
 Inverse problems
 Sparsity regularization
 Microwave imaging
 Accelerated steepest descent
 Artificial neural network