Research on Scatter Imaging Method for Electromagnetic Field Inverse Problem Based on Sparse Constraints

Siying Wu, Huilin Zhou
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Abstract

Since the dimension of the measured scattering field is usually much smaller than the dimension of the unknown parameter, this makes the electromagnetic field integral equation ill-conditioned, and the solution of the equation can be obtained using sparse constraint regularization. For this reason, this paper introduced a nonlinear electromagnetic field inverse scattering imaging algorithm under sparse domain, namely: sparse constraints subspace optimization method (SP-SOM) algorithm, the algorithm is used to reconstruct the spatial distribution information of electrical performance parameters of multi-media targets. To use the inexact Newton method, it can be handled that the scattered field equations is reconstructed using the SP-SOM algorithm. The simulation results show that SP-SOM algorithm can effectively reconstruct the spatial distribution information of electrical performance parameters.
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基于稀疏约束的电磁场反问题散射成像方法研究
由于被测散射场的维数通常远小于未知参数的维数,这使得电磁场积分方程是病态的,可以使用稀疏约束正则化方法得到方程的解。为此,本文引入了一种稀疏域下的非线性电磁场逆散射成像算法,即:稀疏约束子空间优化法(SP-SOM)算法,该算法用于重建多媒体目标电性能参数的空间分布信息。采用不精确牛顿法,可以采用SP-SOM算法对散射场方程进行重构。仿真结果表明,SP-SOM算法可以有效地重构电性能参数的空间分布信息。
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