Browsing by Subject "Ground Penetrating Radar"
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Item Open Access Shallow buried object detection using GPR(Bilkent University, 1999) Kamacı, NejatGround Penetrating Radar received considerable attention in the use of shallow buried object detection. Differing from the traditional sensor systems such as electro magnetic induction based metal detector sensor, GPR can be used for objects with any property and any shape for a wide range of desired sensitivity and specificity. In this thesis, based on a simplified but robust measurement, model a three-stage algorithm is proposed for the real-time detection and localization of the shallow buried objects by using GPR measurements. Since all three stages of the proposed approach have environment adaptive features, the detection performance remains successful in a wide range of scenarios that can be encountered in applications.Item Open Access Sparse ground-penetrating radar imaging method for off-the-grid target problem(SPIE, 2013) Gurbuz, A. C.; Teke, O.; Arıkan, OrhanSpatial sparsity of the target space in subsurface or through-the-wall imaging applications has been successfully used within the compressive-sensing framework to decrease the data acquisition load in practical systems, while also generating highresolution images. The developed techniques in this area mainly discretize the continuous target space into grid points and generate a dictionary of model data that is used in image-reconstructing optimization problems. However, for targets that do not coincide with the computation grid, imaging performance degrades considerably. This phenomenon is known as the off-grid problem. This paper presents a novel sparse ground-penetrating radar imaging method that is robust for off-grid targets. The proposed technique is an iterative orthogonal matching pursuit-based method that uses gradientbased steepest ascent-type iterations to locate the off-grid target. Simulations show that robust results with much smaller reconstruction errors are obtained for multiple off-grid targets compared to standard sparse reconstruction techniques. © 2013 SPIE and IS&T.