Marine Environment RCS Prediction Model Based on Neural Network

The simulation of electromagnetic scattering from the random rough sea surface has a wide range of applications in the study of remote sensing instrument parameter design, ocean parameter inversion, and composite electromagnetic scattering. By introducing the Finite-Difference Time-Domain (FDTD) method, a method based on Generative Adversarial Networks (GANs) is proposed to obtain the radar cross-section (RCS) of targets on the random rough sea surface. This method involves training a GAN model to learn the complex nonlinear relationship between the input sea surface roughness and radar scattering cross-section, thereby achieving high-precision prediction in HH polarization mode. Experimental results show that compared with the FDTD method, the proposed deep learning model not only ensures the accuracy of HH polarization radar scattering cross-section prediction but also significantly reduces computational time.