Efficient Solution to Electromagnetic Scattering Problems of Bodies of Revolution by Compressive Sensing

Kong, Meng; Chen, Mingsheng; Zhang, Liang; Cao, Xinyuan; Wu, Xianliang

doi:10.1088/0256-307x/33/1/018402

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…where [V] ×1 is the excitation vector including N segments, [I] ×1 denotes currents vector which is of the same size as excitation vector, and Z is × impedance matrix. For body of revolution (BOR), BOR-MOM can transform (1) into two scalar linear integral equations in two tangential directions along the generator curve of BOR [13], respectively, and the matrix form can be given by…”

Section: Theory and Formulationmentioning

confidence: 99%

Application of Compressive Sensing to Asymptotic Waveform Evaluation for Fast Frequency-Sweep Analysis of Electromagnetic Scattering Problems

Kong

Chen

Cao

et al. 2019

International Journal of Antennas and Propagation

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To reduce the computing resource of full-scale impedance matrix and its high-order derivatives in traditional Asymptotic Waveform Evaluation (AWE), compressive sensing (CS) is applied to AWE for fast and accurate frequency-sweep analysis of electromagnetic scattering problems. In CS framework, some prior knowledge is extracted by constructing and solving undetermined equation of 0-order surface induced current, so that coefficients about high-order induced current can be accurately obtained by the prior knowledge, and finally the wide-band radar cross section (RCS) is calculated. Numerical results of two-dimensional objects and bodies of revolution (BOR) were presented to the show the efficiency of the proposed method.

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Section: Theory and Formulationmentioning

confidence: 99%

Application of Compressive Sensing to Asymptotic Waveform Evaluation for Fast Frequency-Sweep Analysis of Electromagnetic Scattering Problems

Kong

Chen

Cao

et al. 2019

International Journal of Antennas and Propagation

Self Cite

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“…(15) Where: V n and I n : The excitation and unknown coefficients matrix Z n : The impedance matrix of the body given by Kong et al (2016)…”

Section: J Eng Applied Sci 15 (1): 335-340 2020mentioning

confidence: 99%

The Effect of the Dimensions of the Body on the Radar Cross Section (RCS)

Mohammed¹

2019

J. Eng. Appl. Sci.

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Scattering is the reflection of electromagnetic radiation in various directions, as a result of gas and dust particles located in space. The form of electromagnetic scattering problem for the Body of Revolution (BOR) that are generated by the rotation of an axis called the axis of symmetry because of the symmetry advantages, surface currents can be represented by a fourier series to express the basic function of the current and the test function. Scattering problem can be solved by the equations of the Electric Field Integral Equation (EFIE) and Magnetic Field Integral Equation (MFIE), Galerkin approach was chosen as a method of calculation in this field. The principle of this method depends on the choice of the weighting function that is equal to the complex conjugate of the basis function. The basis function is selected accurately to obtain good results for the scattering problem which is difficult to solve theoretically due to the special boundary conditions for this problem, This method requires a large number of basis functions and testing functions to suit the exact solution. in addition to the Method of Moments (MOM) which is a numerical method for solving electrical and magnetic integrals equations which can calculate the Radar Cross Section (RCS) for the conducting bodies.

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“…Find a reconstruction method with low reconstruction time and good robustness while the recon-struction quality is ensured. In the reconstruction method, the convex relaxation method, [5,6] the greedy matching pursuit method, [7][8][9][10] and the Bayesian method [11][12][13] are usually used to solve the corresponding sparse coding problem. However, the real image does not accurately satisfy the sparsity in the transform domain, the image reconstructed by the sparse modeling reconstruction algorithm is not high in quality.…”

Section: Introductionmentioning

confidence: 99%

An image compressed sensing algorithm based on adaptive nonlinear network*

Guo¹,

Chen²,

Jing³

2020

Chinese Phys. B

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Traditional compressed sensing algorithm is used to reconstruct images by iteratively optimizing a small number of measured values. The computation is complex and the reconstruction time is long. The deep learning-based compressed sensing algorithm can greatly shorten the reconstruction time, but the algorithm emphasis is placed on reconstructing the network part mostly. The random measurement matrix cannot measure the image features well, which leads the reconstructed image quality to be improved limitedly. Two kinds of networks are proposed for solving this problem. The first one is ReconNet's improved network IReconNet, which replaces the traditional linear random measurement matrix with an adaptive nonlinear measurement network. The reconstruction quality and anti-noise performance are greatly improved. Because the measured values extracted by the measurement network also retain the characteristics of image spatial information, the image is reconstructed by bilinear interpolation algorithm (Bilinear) and dilate convolution. Therefore a second network USDCNN is proposed. On the BSD500 dataset, the sampling rates are 0.25, 0.10, 0.04, and 0.01, the average peak signal-noise ratio (PSNR) of USDCNN is 1.62 dB, 1.31 dB, 1.47 dB, and 1.95 dB higher than that of MSRNet. Experiments show the average reconstruction time of USDCNN is 0.2705 s, 0.3671 s, 0.3602 s, and 0.3929 s faster than that of ReconNet. Moreover, there is also a great advantage in anti-noise performance.

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Efficient Solution to Electromagnetic Scattering Problems of Bodies of Revolution by Compressive Sensing

Cited by 8 publications

References 15 publications

Application of Compressive Sensing to Asymptotic Waveform Evaluation for Fast Frequency-Sweep Analysis of Electromagnetic Scattering Problems

Application of Compressive Sensing to Asymptotic Waveform Evaluation for Fast Frequency-Sweep Analysis of Electromagnetic Scattering Problems

The Effect of the Dimensions of the Body on the Radar Cross Section (RCS)

An image compressed sensing algorithm based on adaptive nonlinear network*

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