A Machine Learning Method for 2-D Scattered Far-Field Prediction Based on Wave Coefficients

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.