Performance analysis of subsurface/through the wall imaging techniques and improvement methods

Subsurface or through the wall imaging can be divided to sections such as one layer, two layer and multiple layers. Work with one layer can be described as imaging of the homogeneous field, work with two layer can be described as subsurface imaging and work with multiple layer can be described as throughthe wall imaging. Ground penetrating radar (GPR) is the most known example of subsurface imaging. Several different imaging algorithms for GPR applications is exist. Sparsity based imaging methods from these are becoming significantlyimportant. The most common sparsity based imaging algorithm is compressive sensing (CS). Compressive sensing is a very useful algorithm that is used in ground penetrating radar or through the wall imaging (TWI). CS generates high resolution images by decreasing the required measurement number. Althoughthere are several recent works about TWI with CS, they assume that targets are point like positioned at only discrete grid locations and the wall parameters are perfectly known. However, in real applications, it is not possible to know the exacttarget positions or estimate the wall parameters exactly. The other important issue about the beyond obstacle imaging is the problems of the 3D GPR imaging. These problems are high dictionary size and computational complexity problems.This work represents a data module which realizes several different imaging algorithm and it have capability to compare and control the GPR datas that they have. In addition details the theory for CS based TWI and the imaging ability ofCS are detailed and the effect of errors in unknown parameters on the imaging performance is discussed. Finally, this thesis demonstrates a new approach for 3D GPR imaging and analyzes its theory and compare the performance of the proposed method with previous techniques.