Diagnosis of Array Antennas Based on Phaseless Near-Field Data Using Artificial Neural Network

Xin Wang, Nonmember Keisuke Konno, Qiang Chen

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Diagnosis of array antennas based on phaseless near-field data is a practically important non-linear inverse problem. One of the biggest challenges for the non-linear inverse problem is to alleviate an ill-posedness resulting in poor accuracy. In this paper, a novel source reconstruction method, which is based on an artificial neural network (ANN) enhanced by eigenmode currents is proposed. Eigenmode currents, that 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 as 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 authors knowledge, this is the first paper demonstrating effectiveness of the eigenmode currents on the non-linear inverse problem.

Original languageEnglish
JournalIEEE Transactions on Antennas and Propagation
Publication statusAccepted/In press - 2020


  • Antenna diagnosis
  • array antenna
  • artificial neural network
  • current distribution
  • inverse problem
  • near field
  • source reconstruction

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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