A hybrid artificial intelligence framework for three-dimensional electromagnetic field mapping using airborne measurements in general aviation

The increasing electrification of aircraft systems and development of autonomous aviation technologies increase the need for accurate characterization of electromagnetic field (EMF) distributions under operational conditions. However, dense three-dimensional EMF measurements in aviation environments are difficult to obtain because airborne observations are typically sparse, irregularly distributed, and collected under varying flight conditions. Here, we present an artificial intelligence-based framework for three-dimensional reconstruction of the electric field component of EMF from real-world airborne measurements in general aviation. The study uses more than 80 h of measurements collected during operational activities involving four aircraft types and a low-cost, high-sensitivity sensing platform recording synchronized electric-field and geospatial data over the 1-8000 MHz frequency range. Geostatistical and machine-learning approaches, including Kriging, Kolmogorov-Arnold Networks, and Exact Gaussian Processes, were evaluated for three-dimensional field reconstruction. Based on this comparison, we developed a hybrid Deep Gaussian Process Kolmogorov-Arnold Feature Extractor (DGKAN), combining nonlinear feature representation with probabilistic spatial modeling and uncertainty estimation. DGKAN achieved the highest predictive accuracy, with a mean absolute error of 0.0381 and a mean squared error of 0.0071, reducing the mean absolute error by approximately 27.6% relative to the strongest baseline model. These results demonstrate that combining Kolmogorov-Arnold representations with probabilistic Gaussian Process modeling enables accurate reconstruction of three-dimensional electromagnetic field distributions from sparse airborne measurements. The proposed framework provides a data-driven approach for electromagnetic environment assessment in general aviation and establishes a basis for further development of intelligent EMF monitoring and prediction methods under operational flight conditions.