Browsing by Subject "Matching pursuit algorithms"

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    SAR image reconstruction by EMMP algorithm
    (IEEE, 2012-04) Uǧur, Salih; Arıkan, Orhan; Gürbüz, A. C.
    In this work, EMMP algorithm is used to solve the SAR image reconstruction problem which is modelled in the compressed sensing context. It is found that the sparsity parameter of the target region is an important parameter determining the quality of the output image. The proposed method is applied to the real SAR data and provided high quality outputs. © 2012 IEEE.
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    SAR image reconstruction by expectation maximization based matching pursuit
    (Academic Press, 2015) Ugur, S.; Arıkan, Orhan; Gürbüz, A. C.
    Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching Pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation.