Programmable metasurface RCS prediction under obstacles based on DNN

Programmable metasurfaces have attracted significant attention in various applications such as radar and 6G communications, owing to their ability freely shape the far-field pattern. However, complex calculations and simulations are always required when designing specific far-field patterns, especially when irregular obstacles are outside the metasurface. In this article, we propose a method using a four-layer artificial neural network to realize the far-field radar cross section (RCS) prediction of programmable metasurfaces in an environment with obstacles, and the prediction value agreed with the simulation data reasonably well. Results show that the proposed prediction model is characterized by better learning and generalization capacity. Our work has broad application prospects and value in complex environment signal transmission, metasurface inverse design, etc.