Advancing microwave imaging algorithms and techniques

作     者：Bayat, Nozhan 

作者单位：University of Manitoba 

学位级别：博士

导师姓名：Mojabi, Puyan

授予年度：2019年

主      题：Microwave imaging Microwave tomography Imaging algorithms 

摘      要：Thisdissertation studies and develops novel techniques and algorithms in the area of mi- crowave imaging (MWI). In MWI, the objective is to create a (quantitative) image of the dielectric profile of the object of interest (OI) in a non-destructive fashion. To this end, the OI is interrogated using non-ionizing and relatively low-power microwaves which are gen- erated by some antennas. These incident microwaves will then interact with the OI, and consequently scattered electromagnetic fields will arise which contain information about the OI. These scattered fields will be collected, and then processed to extract their information so as to create the final image. This process is often referred to as the inversion of the scattered field data to reconstruct the OI s dielectric profile. Currently, MWI faces some challenges that limit its capability to become a widely-accepted imaging tool. Three of these challenges are: (i) lack of fundamental understanding about the relation between the measured scattered fields and the achievable image accuracy and resolu- tion, (ii) insufficient image accuracy and resolution for some applications, and (iii) difficulty to collect sufficient measured data due to various practical limitations. The main focus of this dissertation is to investigate and develop techniques and algorithms in an attempt to address these three challenges. This dissertation begins by introducing the concept of best possible reconstruction from given MWI configurations. This concept is important since if the best possible reconstruc- tion fails to provide the features of interest, the actual blind reconstruction will not be able to provide these features either. To improve the achievable reconstruction, one option is to in- ject prior information into the algorithm. To this end, a fully automated inversion algorithm is presented that is able to incorporate prior spatial (structural) information about the OI. The proposed algorithm, which is capable of working w