Author:
Cao Le,Hao Shuai,Zhao Yuan,Wang Cheng
Abstract
This study presents a prediction model for high-power electromagnetic pulse (HPEMP) effects on aboveground vehicles based on convolutional neural networks (CNNs). Since a vehicle is often located aboveground and is close to the air-ground–half-space interface, the electromagnetic energy coupled into the vehicle by the ground reflected waves cannot be ignored. Consequently, the analysis of the vehicle’s HPEMP effect is a composite electromagnetic scattering problem of the half-space and the vehicles above it, which is often analyzed using different half-space numerical methods. However, traditional numerical methods are often limited by the complexity of the actual half-space models and the high computational demands of complex targets. In this study, a prediction method is proposed based on a CNN, which can analyze the electric field and energy density under different incident conditions and half-space environments. Compared with the half-space finite-difference time-domain (FDTD) method, the accuracy of the prediction results was above 98% after completing the training of the CNN network, which proves the correctness and effectiveness of the method. In summary, the CNN prediction model in this study can provide a reference for evaluating the HPEMP effect on the target over a complex half-space medium.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Natural Science Basic Research Plan in Shaanxi Province of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering