ADVANCED TECHNIQUES FOR SYNTHETIC APERTURE RADAR IMAGE RECONSTRUCTION By DUC H. VU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ADVANCED TECHNIQUES FOR SYNTHETIC APERTURE RADAR IMAGE RECONSTRUCTION By Duc H. Vu May 2012 Chair: Jian Li Major: Electrical and Computer Engineering This dissertation discusses advanced aspects of Synthetic Aperture Radar (SAR). SAR is a crucial capability for radar systems in both civilian as well as government sectors. This dissertation focuses on two actively researched aspects of SAR systems. First, a framework for reconstructing SAR images from a spectral analysis perspective is introduced. This approach is more robust and more accurate due to lower sidelobes level and can be used in situations that full data collection is not permissible. Second, a framework for identifying movers directly from SAR data is also discussed. Furthermore, this framework takes in account SAR systems with multiple antennas and is shown to offer superior performance in such cases. The dissertation also provides a basic overview of the signal processing involve in constructing SAR images. This background knowledge lays the foundation to which further advanced concepts are built upon. We first focus on a new approach to reconstructing SAR images. This new approach is from the spectral estimation community. In particular we utilize the Bayesian framework as it results in a sparser SAR image that is more amenable for identification and classification purposes. We utilize an algorithm named SLIM, which can be thought of as a sparse signal recovery algorithm with excellent sidelobes suppression and high resolution properties. For a given sparsity promoting prior, SLIM cyclically minimizes a regularized least square cost function. We show how SLIM can be used for SAR image reconstruction as well as used for SAR image enhancement. We evaluate