Diagnosis of Array Antennas Based on Phaseless Near-Field Data Using Artificial Neural Network
Diagnosis of Array Antennas Based on Phaseless Near-Field Data Using Artificial Neural Network
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DOI：
10.1109/tap.2020.3044593
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发表时间：
2021-07
影响因子：
5.7
通讯作者：
Xin Wang;K. Konno;Qiang Chen
中科院分区：
文献类型：
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作者：
Xin Wang;K. Konno;Qiang Chen
基于无相近场数据的阵列天线故障诊断是一个重要的非线性反问题。非线性反问题的最大挑战之一是减轻不适定性导致的低精度。本文提出了一种基于本征模电流增强的人工神经网络（ANN）的新型源重构方法。本征模电流作为一种宏基函数，在阵列天线精确几何形状确定后，可以通过数值计算得到，本文提出的源重构方法称为ANN-EC（eigenmode currents），并将其应用于基于无相位近场数据的阵列天线故障诊断。与传统的基于人工神经网络的信源重构方法相比，该方法具有两个优点：一是精度高，二是对噪声具有鲁棒性。这两个优点都源于使用源重构减少本征模电流的数量。重建电流的准确性进行了评估，并证明了这些优势的ANN-EC比传统的人工神经网络。据作者所知，这是第一篇证明本征模电流对非线性逆问题有效性的论文。
Diagnosis of array antennas based on phaseless near-field data is a practically important nonlinear inverse problem. One of the biggest challenges for the nonlinear inverse problem is to alleviate an ill-posedness resulting in poor accuracy. In this article, a novel source reconstruction method, which is based on an artificial neural network (ANN) enhanced by eigenmode currents, is proposed. Eigenmode currents, which work as macro basis functions, can be obtained numerically once precise geometry of the array antennas is found. The proposed source reconstruction method is named an ANN-EC (eigenmode currents) and is applied to diagnosis of array antennas based on phaseless near-field data. The ANN-EC has two advantages over conventional source reconstruction techniques purely based on the ANN. The first one is enhancement of accuracy and the second one is robustness to noise. Both of these advantages stem from reduction of the number of eigenmode currents using source reconstruction. Accuracy of the reconstructed currents is evaluated and these advantages of the ANN-EC over conventional ANN are demonstrated. To the best of the authors’ knowledge, this is the first paper demonstrating the effectiveness of the eigenmode currents on the nonlinear inverse problem.