An AI Predictor: From Point Clouds to Scattered Far Fields for 3-D PEC Targets
De-Hua Kong, Jianing Cao, Wenwei Zhang, Wen-Chi Huang, Xiaoyang He, Lu Liu, Ming‐Yao Xia
Abstract
In this paper, an artificial intelligent (AI) approach is proposed for the prediction of scattered far fields by 3-D perfect-electrical-conducting (PEC) targets. The conventional computational electromagnetics (CEM) methods are plagued by at least two issues. One is the mesh generation, and the other is the intensive or time-consuming solution of matrix equations which could be unbearable. Machine learning (ML) methods provide a new perspective to tackle the problems by skipping the two steps. Point clouds can well describe the geometrical information of a radar target. Directly using the point cloud rather than the created mesh with the point data should be workable, so we will take the point cloud as the input of the neural network (NN). As for the output, we will let the NN extract a kind of inherent feature parameters (IFPs) that are distinctive to each target and independent of incident and scattering directions. The characteristic mode theory is employed to acquire the IFPs for the generation of data sets. The scattered far field can be readily computed using the IFPs when the incident and scattering directions are specified. We call the NN constructed to realize the function Electromagnetic PointNet (EMPN). Numerical examples show that the EMPN can achieve remarkable reductions in CPU time and even memory requirements. The proposed approach is convenient to run from the input of a point cloud file that may be obtained by using a cellphone with a depth camera to output of scattered far field or radar cross-section (RCS).