As a non-iterative linear inverse scattering imaging method, the linear sampling method (LSM) has the merits of easy implementation and high computational efficiency. In this article, based on the LSM and T-matrix method, we propose a fast method to reconstruct the target medium parameters. First, we use the LSM qualitative imaging method to estimate the contour of the target. Then, the preprocessing information of the LSM is applied to the virtual experiment circles in the framework of T-matrix method. In this way, we avoid the inversion of the matrix and only need to solve a series of univariate equations. Numerical simulation results show that the reconstruction accuracy of the proposed method is similar to that of LSM quantitative imaging method, but it has higher calculation accuracy and wider application range. Although the calculation accuracy is not as good as the contrast source inversion (CSI) method, the proposed method has huge advantages in terms of calculation efficiency. INDEX TERMS Electromagnetic inverse scattering, linear sampling method, microwave imaging, Tmatrix, virtual experiments
In this paper, an improved T-matrix method is proposed to improve the accuracy for simultaneous reconstruction of dielectric scatterers and perfectly electric conductors (PEC). First, we modify the original T-matrix inversion method and obtain a preliminary reconstruction result. Then treat this result as a priori information, and a new initial iteration value is generated by truncating and assigning values to it. At the same time, genetic algorithm (GA) is used to optimize these truncation and assignment parameters. Finally, the optimization result is used as the initial value of the modified T-matrix method to obtain better reconstruction accuracy. The numerical simulation results under noisy conditions show that the proposed method can more clearly reconstruct the edges of the objects and have higher reconstruction accuracy. INDEX TERMS Electromagnetic inverse scattering, T-matrix, genetic algorithm (GA), microwave imaging, high accuracy
Accurate inversion of high-contrast objects is of great interest to many researchers. In this paper, we reconstruct sparse high-contrast targets perfectly based on the joint sparse reconstruction and the contrast source inversion (CSI). First, the targets number is estimated accurately with the minimum description length (MDL) criterion. Second, with the exact targets number as a priori information, the supports of the targets are perfectly recovered based on the joint-sparse structure of the contrast sources under a multiple measurement vector (MMV) scheme. Finally, the contrast is perfectly reconstructed with the CSI method, in which a priori information about the accurate supports is added. The perfect mask is such strong a priori information that the reconstruction is enforced to locate on the positions of real targets, enormously enhancing the rebuilding quality. Perfect reconstructions of sparse objects with high contrast are demonstrated under various scenarios, showing effectiveness and robustness of the proposed method. Moreover, limitations of the proposed method are discussed, which explains the difference of success rate of accurate reconstruction with different mesh sizes from a physical insight. INDEX TERMS Compressive sensing, contrast source inversion (CSI), electromagnetic inverse scattering, joint sparse, perfect mask.
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