Compressive sensing for subsurface imaging using ground penetrating radar

Gürbüz, Ali Cafer; McClellan, J.H.; Scott, Waymond R.

doi:10.1016/j.sigpro.2009.03.030

Cited by 119 publications

(84 citation statements)

References 21 publications

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“…The subsurface image was generated by solving an l 1 minimizationbased optimization problem with a decreased number of measurements. Later, these results were extended to the stepped frequency 10 and impulse GPR 11 cases. In Ref.…”

Section: Introductionmentioning

confidence: 95%

Sparse ground-penetrating radar imaging method for off-the-grid target problem

Gürbüz

Teke

Arıkan

2013

J. Electron. Imaging

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Abstract. Spatial 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.

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Section: Introductionmentioning

confidence: 95%

Sparse ground-penetrating radar imaging method for off-the-grid target problem

Gürbüz

Teke

Arıkan

2013

J. Electron. Imaging

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“…by the above mentioned solvers or MP. Algorithms also exist to optimize the value of ε and avoid cases of noise overfitting or under-fitting [17].…”

Section: Compressive Sensingmentioning

confidence: 99%

Compressive System Identification in the Linear Time-Invariant framework

Tóth

Sanandaji

Poolla

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in parametric system identification. It navigates a trade-off between representation capabilities of the model (structural bias) and effects of over-parametrization (variance increase of the estimates). There exists many approaches to this widely studied problem in terms of statistical regularization methods and information criteria. In this paper, an alternative ℓ1 regularization scheme is proposed for estimation of sparse linear-regression models based on recent results in compressive sensing. It is shown that the proposed scheme provides consistent estimation of sparse models in terms of the so-called oracle property, it is computationally attractive for large-scale over-parameterized models and it is applicable in case of small data sets, i.e., underdetermined estimation problems. The performance of the approach w.r.t. other regularization schemes is demonstrated in an extensive Monte Carlo study.

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“…Recently, compressive sensing (CS) has been used for efficient data acquisition in radar systems in general [10][11][12][13][14] and in urban radar systems in particular [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Partially sparse imaging of stationary indoor scenes

Ahmad

Amin

Dogaru

2014

EURASIP J. Adv. Signal Process.

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In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that the scene being imaged consists of a sparse part and a dense part, with the support of the latter assumed to be known. For the problem at hand, sparsity is represented by a few stationary indoor targets, whereas the high scene density is defined by exterior and interior walls. Prior knowledge of wall positions and extent may be available either through building blueprints or from prior surveillance operations. The contributions of the exterior and interior walls are removed from the data through the use of projection matrices, which are determined from wall-and corner-specific dictionaries. The projected data, with enhanced sparsity, is then processed using l 1 norm reconstruction techniques. Numerical electromagnetic data is used to demonstrate the effectiveness of the proposed approach for imaging stationary indoor scenes using a reduced set of measurements.

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Compressive sensing for subsurface imaging using ground penetrating radar

Cited by 119 publications

References 21 publications

Sparse ground-penetrating radar imaging method for off-the-grid target problem

Sparse ground-penetrating radar imaging method for off-the-grid target problem

Compressive System Identification in the Linear Time-Invariant framework

Partially sparse imaging of stationary indoor scenes

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