On the Calibration of a Multistatic Scattering Matrix Measured by a Fixed Circular Array of Antennas

Litman, Amélie; Geffrin, Jean-Michel; Tortel, Hervé

doi:10.2528/pier10090302

Cited by 20 publications

(20 citation statements)

References 31 publications

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“…In all these cases the amount of memory requirement for inversion calculation will increase significantly, therefore iterative methods become more suitable techniques for large scale computation. The same problem may occur in a number of other inverse problems [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 91%

Three-Dimensional Magnetic Induction Tomography Imaging Using a Matrix Free Krylov Subspace Inversion Algorithm

Wei¹,

Soleimani²

2012

PIER

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Abstract-Magnetic induction tomography (MIT) attempts to image the passive electromagnetic properties (PEP) of an object by measuring the mutual inductances between pairs of coils placed around its periphery. In recent years, there has been an increase in applications of non-contact magnetic induction tomography. When finite element-based reconstruction methods are used, that rely on the inversion of a derivative operator, the large size of the Jacobian matrix poses a challenge since the explicit formulation and storage of the Jacobian matrix could be in general not feasible. This problem is aggravated further in applications for example when the number of coils is increased and in three-dimension. Krylov subspace methods such as conjugate gradient (CG) methods are suitable for such large scale inverse problems. However, these methods require use of the Jacobian matrix, which can be large scale. This paper presents a matrix-free reconstruction method, that addresses the problems of large scale inversion and reduces the computational cost and memory requirements for the reconstruction. The idea behind the matrixfree method is that information about the Jacobian matrix could be available through matrix times vector products so that the creation and storage of big matrices can be avoided. Furthermore the matrix vector multiplications were performed in multiple core fashion so that the computational time can decrease even further. The method was tested for the simulated and experimental data from lab experiments, and substantial benefits in computational times and memory requirements have been observed.

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Section: Introductionmentioning

confidence: 91%

Three-Dimensional Magnetic Induction Tomography Imaging Using a Matrix Free Krylov Subspace Inversion Algorithm

Wei¹,

Soleimani²

2012

PIER

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“…However, there parameters give only a rough estimate of the general category of the actual target type. The reflected signal of the radar target can be simulated using scattering analysis of the radar target [36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In addition to radar detection and tracking, there have been mamy studies on radar target recognition [3,7,[50][51][52][53][54][55][56][57][58][59][61][62][63][64][65][66][67][68][69][70].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Radar Target Recognition Using Natural Frequency: Frequency Domain Approach

Lee¹,

Cho²,

Park³

et al. 2012

PIER

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Abstract-We consider the performance analysis of natural frequency-based radar target recognition in the frequency domain. Based on the probability density function (PDF) of some quantity consisting of the projections of the frequency response onto the column spaces of the matrices constructed using the natural frequencies of the specific targets, we propose to analytically calculate the probability of the correct classification, where the PDF is obtained from the inverse Fourier transform of the characteristic function. The scheme is validated by comparing the performance using the analytic method with that using the Monte-Carlo simulation.

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“…Some studies are focused on the impedance computation by various modeling methods. The computed mutual impedance can be used to reduce the radar cross section (RCS) or synthesize the optimal radiation pattern of the conformal array [7][8][9][10][11][12][13][14]. Also, it is useful for an interaction between the dipole particles, for example, analyzing the array impedance [15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Simple Formula and Its Exact Analytic Solution of Mutual Impedance for Nonplanar-Skew Dipoles

Han¹,

Song²,

Oh³

et al. 2012

PIER

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Abstract-The analysis of an antenna mutual coupling is a significant issue for designing the wireless communication system especially includesan array mutual coupling problem. The accurate analysis of the mutual coupling between antennas is needed. Accordingly, several methods for the mutual impedance calculation of dipoles have been studied in cases of coplanar-skew and nonplanar-skew. This paper proposes an exact and simple method for analyzing the mutual impedance between two arbitrarily located and slanted dipoles using the modified induced EMF method; their expressions and the exact analytic solution. The proposed formula and their closed-form solutions are verified by numerical solution using HFSS and give good agreement.

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On the Calibration of a Multistatic Scattering Matrix Measured by a Fixed Circular Array of Antennas

Cited by 20 publications

References 31 publications

Three-Dimensional Magnetic Induction Tomography Imaging Using a Matrix Free Krylov Subspace Inversion Algorithm

Three-Dimensional Magnetic Induction Tomography Imaging Using a Matrix Free Krylov Subspace Inversion Algorithm

Performance Analysis of Radar Target Recognition Using Natural Frequency: Frequency Domain Approach

Simple Formula and Its Exact Analytic Solution of Mutual Impedance for Nonplanar-Skew Dipoles

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