3d Sliced Tomographic Inverse Scattering Experimental Results

Solimene, Raffaele; Brancaccio, Adriana; Napoli, Rosario; Pierri, Rocco

doi:10.2528/pier10050705

Cited by 20 publications

(20 citation statements)

References 26 publications

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“…For example, an iterative three-dimensional (3D) algorithm was applied for detecting breast tumors upon using time-domain microwave data [1]; a nonlinear multisource strategy was presented for the quantitative imaging of two-dimensional (2D) scatterers [2]; and a 2D sliced tomographic reconstruction algorithm was used for imaging 3D strong scatterers validated using experimental data [3].…”

Section: Introductionmentioning

confidence: 99%

Inverse Scattering of Three-Dimensional Pec Objects Using the Level-Set Method

Hajihashemi¹,

El-Shenawee²

2011

PIER

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Abstract-A 3D shape reconstruction algorithm for multiple PEC objects immersed in air is presented. The Hamilton-Jacobi PDE is solved in the entire computational domain with employing the marching cubes method to retrieve the unknown objects. The method of moment surface integral equation is used as the forward solver. An appropriate form of the deformation velocity, based on the forward and adjoint fields, is implemented to minimize the mismatch between the measurements and simulated scattered fields from the evolving objects. The inversion algorithm demonstrated good shape reconstruction results even when using limited view data or noisy corrupted data with SNR of 5 dB.

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Section: Introductionmentioning

confidence: 99%

Inverse Scattering of Three-Dimensional Pec Objects Using the Level-Set Method

Hajihashemi¹,

El-Shenawee²

2011

PIER

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“…For some applications where the axial conductivity change is significant, this assumption is unrealistic and the 2D imaging is no longer applicable. In order to extract the variation in axial direction, 3D images are required [11][12][13]. 3D MIT is valuable for imaging the volumetric distribution of electrical conductivity and believed to be the future expansion trend of MIT applications.…”

Section: Magnetic Induction Tomography (Mit) Is An Electromagnetic Immentioning

confidence: 99%

Four Dimensional Reconstruction Using Magnetic Induction Tomography: Experimental Study

Wei¹,

Soleimani²

2012

PIER

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Abstract-Magnetic Induction Tomography (MIT) is a relatively new and emerging type of tomography techniques that is able to map the distribution of all three passive electrical properties (PEPs). Its non-invasive and contactless features make it an attractive technique for many applications compared to the traditional contact electrode based electrical impedance tomography. Recently, MIT has become a promising monitoring technique in industrial process tomography, and the area of the research interest has moved from 2D to 3D because of the volumetric nature of electromagnetic field. Three dimensional MIT images provide more information on the conductivity distribution, especially in the axial direction. However, it has been reported that the reconstructed 3D images can be distorted when the imaging object is located at a less sensitive region. Although this distortion can be compensated by adjusting the regularisation criteria, this is not practical in real life applications as the prior information about the object's location is often unavailable. This paper presents a memory efficient 4D MIT algorithm which can maintain the image quality under the same regularisation circumstances. Instead of solving each set of measurement individually, the 4D algorithm takes advantage of the correlations between the image and its neighboring data frames to reconstruct 4D of conductivity movements. The 4D algorithm improves the image qualities by increasing the temporal resolution. It also overcomes some sensitivity issues of 3D MIT algorithms and can provide a more stable result in terms of the size consistency of the reconstructed image. Several experimental results using real laboratory data are presented for validating the proposed algorithms.

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“…But InSAR cannot extract the heights of different scatterers in the same range-azimuth cell. In the past decade, Tomographic SAR (TomoSAR) technique has been presented, which extends the synthetic aperture principle into the elevation by slightly different multi-pass track [8,9]. TomoSAR allows to extract different height scatterers in each given rangeazimuth cell and provides the true 3-D images of illuminated scene.…”

Section: Introductionmentioning

confidence: 99%

Linear Array Sar Imaging via Compressed Sensing

Wei¹,

Zhang²,

Shi³

2011

PIER

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Abstract-In recent years, various attempts have been undertaken to obtain three-dimensional (3-D) reflectivity of observed scene from synthetic aperture radar (SAR) technique.Linear array SAR (LASAR) has been demonstrated as a promising technique to achieve 3-D imaging of earth surface. The common methods used for LASAR imaging are usually based on matched filter (MF) which obeys the traditional Nyquist sampling theory. However, due to limitation in the length of linear array and the "Rayleigh" resolution, the standard MFbased methods suffer from low resolution and high sidelobes. Hence, high resolution imaging algorithms are desired. In LASAR images, dominating scatterers are always sparse compared with the total 3-D illuminated space cells. Combined with this prior knowledge of sparsity property, this paper presents a novel algorithm for LASAR imaging via compressed sensing (CS). The theory of CS indicates that sparse signal can be exactly reconstructed in high Signal-NoiseRatio (SNR) level by solving a convex optimization problem with a very small number of samples. To overcome strong noise and clutter interference in LASAR raw echo, the new method firstly achieves range focussing by a pulse compression technique, which can greatly improve SNR level of signal in both azimuth and crosstrack directions. Then, the resolution enhancement images of sparse targets are reconstructed by L1 norm regularization. High resolution properties and point localization accuracies are tested and verified by simulation and real experimental data. The results show that the CS method outperforms the conventional MF-based methods, even if very small random selected samples are used.

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3d Sliced Tomographic Inverse Scattering Experimental Results

Cited by 20 publications

References 26 publications

Inverse Scattering of Three-Dimensional Pec Objects Using the Level-Set Method

Inverse Scattering of Three-Dimensional Pec Objects Using the Level-Set Method

Four Dimensional Reconstruction Using Magnetic Induction Tomography: Experimental Study

Linear Array Sar Imaging via Compressed Sensing

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