Novel methods for SAR imaging problems
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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.
KeywordsSynthetic Aperture Radar
Phase Error Correction
Expectation Maximization Based Matching Pursuit