| Synthetic aperture radar (SAR) has been a mature but actively researched technology due to its day-and-night and all-weather capability of offering high resolution imaging for both military and civilian applications. As the foundation of automatic target detection and recognition, SAR imaging and autofocusing continue to attract more research interest. Non-parametric spectral estimation methods are robust methods for SAR image formation. However, non-parametric methods cannot be used to significantly improve the resolution of the formed SAR images since they generally do not fully exploit the characteristics of radar targets of interests even when such information is available.; In this dissertation, efforts have been made to form super resolution two-dimensional (2-D) SAR images via relaxation-based parametric methods. The relaxation-based optimization methods have been proved to be quite useful in several other applications, such as radio astronomy, microwave imaging, and spectral estimation. The relaxation-based methods are extended for super resolution SAR imaging of radar targets consisting of only trihedrals or both trihedrals and dihedrals. We have also devised a robust and computationally simple SPAR (Semi-PARametric) algorithm for 2-D SAR imaging based on a flexible semi-parametric data model when it is difficult to establish an accurate target data model in cross-range. Hence SPAR takes advantages of both parametric and non-parametric spectral estimation methods to form enhanced SAR images. Numerical and experimental examples have been used to demonstrate the performances of the proposed algorithms. We have observed that the relaxation-based parametric methods provide super resolution SAR images when the assumed data model is valid; otherwise SPAR performs better.; Three-dimensional (3-D) target features, including the height information, radar cross section (RCS), and 2-D location (range and cross-range), provide quite useful information for such applications as automatic target recognition. Thus, efforts have also been made in this dissertation to devise an effective relaxation-based algorithm, referred to as AUTORELAX, for both 3-D target feature extraction and motion compensation via curvilinear SAR (CLSAR), a novel technology which is still at its developing stage. The proposed AUTORELAX algorithm is shown to be promising when evaluated by using both the experimental and simulated examples. |
