Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 62-70.Synthetic Aperture Radar (SAR) provides high resolution images of terrain reflectivity.
SAR systems are indispensable in many remote sensing applications. High
resolution imaging of terrain requires precise position information of the radar
platform on its flight path. In target detection and identification applications,
imaging of sparse reflectivity scenes is a requirement. In this thesis, novel SAR
image reconstruction techniques for sparse target scenes are developed. These
techniques differ from earlier approaches in their ability of simultaneous image
reconstruction and motion compensation. It is shown that if the residual phase
error after INS/GPS corrected platform motion is captured in the signal model,
then the optimal autofocused image formation can be formulated as a sparse
reconstruction problem. In the first proposed technique, Non-Linear Conjugate
Gradient Descent algorithm is used to obtain the optimum reconstruction. To
increase robustness in the reconstruction, Total Variation penalty is introduced
into the cost function of the optimization. To reduce the rate of A/D conversion
and memory requirements, a specific under sampling pattern is introduced. In the
second proposed technique, Expectation Maximization Based Matching Pursuit
(EMMP) algorithm is utilized to obtain the optimum sparse SAR reconstruction.
EMMP algorithm is greedy and computationally less complex resulting in fast
SAR image reconstructions. Based on a variety of metrics, performances of the
proposed techniques are compared. It is observed that the EMMP algorithm has
an additional advantage of reconstructing off-grid targets by perturbing on-grid
basis vectors on a finer grid.Uğur, SalihPh.D