A Deep-Learning-Based Modeling Method for Phase-Free Near-Field Scanning Measurement

This study presents a deep-learning-based method for the precise inversion of electromagnetic (EM) radiation sources, utilizing amplitude data from multichannel near-field scans to infer equivalent dipole array parameters. Unlike traditional heuristic search methods or interpolation-based artificial neural networks (ANNs), the proposed model combines residual networks with channel attention mechanisms to effectively capture the intrinsic nonlinear relationships among multidirectional near-field data and their correlations with dipole parameters, thereby streamlining the inverse mapping process from near-field measurements to dipole parameters. Comprehensive simulations and experimental results, including scenarios with patch antennas at varying heights and microcontroller unit (MCU) chips, validate the robustness and accuracy of the proposed model.