NOVEL OPTIMIZATION METHODS IN MICROWAVE ENGINEERING: APPLICATIONS IN IMAGING AND DESIGN

Abstract

<p>In this thesis, inverse problems related to microwave imaging and microwave component design are investigated. Our contribution in microwave imaging for breast tumor detection can be divided into two parts. In the first part, a vectorial 3D near-field microwave holography is proposed which is an improvement over the existing holography algorithms. In the second part, a simple and fast post-processing algorithm based on the principle of blind de-convolution is proposed for removing the integration effect of the antenna aperture. This allows for the data collected by the antennas to be used in 3D holography reconstruction. The blind deconvolution algorithm is a well-known algorithm in signal processing and our contribution here is its adaptation to microwave data processing.</p> <p>Second, a procedure for accelerating the space-mapping optimization process is presented. By exploiting both fine- and surrogate-model sensitivity information, a good mapping between the two model spaces is efficiently obtained. This results in a significant speed-up over direct gradient-based optimization of the original fine model and enhanced performance compared with other space-mapping approaches. Our approach utilizes commercially available software with adjoint-sensitivity analysis capabilities.</p>