A Novel CS-TSVD Strategy to Perform Data Reduction in Linear Inverse Scattering Problems

Solimene, Raffaele; Ahmad, Fauzia; Soldovieri, Francesco

doi:10.1109/lgrs.2012.2185679

Cited by 31 publications

(21 citation statements)

References 21 publications

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“…For instance, spectral estimation algorithms (e.g., the " SL1MMER" technique) have been proposed by combining ' 1minimization CS steps with a model order reduction and a maximum-likelihood parameter selection [94]. Moreover, combining the NDOF-TSVD approaches [96] and CS techniques has been discussed [95] by formulating the sparse problem at hand as the second-order cone problem [97,98].…”

Section: Tomo-sar Imagingmentioning

confidence: 99%

Compressive Sensing in Electromagnetics - A Review

Massa

Rocca

Oliveri

2015

IEEE Antennas Propag. Mag.

331

143

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International audienceSeveral problems arising in electromagnetics can be directly formulated or suitably recast for an effective solution within the compressive sensing (CS) framework. This has motivated a great interest in developing and applying CS methodologies to several conventional and innovative electromagnetic scenarios. This work is aimed at presenting, to the best of the authors' knowledge, a review of the state-of-the-art and most recent advances of CS formulations and related methods as applied to electromagnetics. Toward this end, a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed. Current challenges and trends in the application of CS to the solution of traditional and new electromagnetic problems are also discussed

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Section: Tomo-sar Imagingmentioning

confidence: 99%

Compressive Sensing in Electromagnetics - A Review

Massa

Rocca

Oliveri

2015

IEEE Antennas Propag. Mag.

331

143

View full text Add to dashboard Cite

show abstract

“…To improve the computational efficiency, truncated singular value decomposition (TSVD) is used to alleviate the computational cost of the conventional CS. Although TSVD does not allow achieving a superresolution, the truncated character can contribute to reduce the redundancy of spatial measurements [12]. The proposed method not only reduces the computational load but also preserves the superresolution in cross-track direction.…”

Section: Introductionmentioning

confidence: 99%

Truncated SVD-Based Compressive Sensing for Downward-Looking Three-Dimensional SAR Imaging With Uniform/Nonuniform Linear Array

Zhang

Zhu

Dong

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios. Index Terms-Compressive sensing (CS), nonuniform linear array (LA), superresolution, truncated singular value decomposition (TSVD), 3-D synthetic aperture radar (SAR).

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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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A Novel CS-TSVD Strategy to Perform Data Reduction in Linear Inverse Scattering Problems

Cited by 31 publications

References 21 publications

Compressive Sensing in Electromagnetics - A Review

Compressive Sensing in Electromagnetics - A Review

Truncated SVD-Based Compressive Sensing for Downward-Looking Three-Dimensional SAR Imaging With Uniform/Nonuniform Linear Array

Partially sparse imaging of stationary indoor scenes

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