A Machine Learning Method for 2-D Scattered Far-Field Prediction Based on Wave Coefficients
Wenwei Zhang, De-Hua Kong, Xiaoyang He, Ming‐Yao Xia
Abstract
In this letter, a machine learning method is presented to evaluate the scattering by 2-D conducting objects. First, the scattered far field is expressed by angular harmonics with weighted wave coefficients (WCs), which are distinctive to the cross-section of the scatterer. Then, a neural network (NN) is trained to learn the WCs from a range of objects. Finally, the NN is used to extract the WCs for a given object, and the scattered far field or radar cross-section is readily computed by using the WCs. Numerical examples show that the proposed approach can be a viable choice for fast online prediction.
Topics & Concepts
Radar cross-sectionScatteringArtificial neural networkField (mathematics)Computer scienceCross section (physics)RadarNear and far fieldRange (aeronautics)HarmonicsPhysical opticsArtificial intelligencePhysicsAlgorithmOpticsMathematicsEngineeringTelecommunicationsAerospace engineeringQuantum mechanicsPure mathematicsVoltageMicrowave Imaging and Scattering AnalysisGeophysical Methods and ApplicationsElectromagnetic Simulation and Numerical Methods