An Experimental Platform to Simulate Real-Time Correlated Sea Clutter Based on Space-Time-Coding Digital Metasurfaces

The traditional sea clutter simulation techniques encounter challenges in effectively balancing dynamic simulation, coupling effects, and construction cost management. To tackle these issues, we propose an experimental platform to simulate the sea clutter in real time using space-time-coding digital metasurfaces. Specifically, we use deep learning techniques to construct an encoding retrieval library with angle-insensitive characteristics. Combing the library with the desired clutter sequences generated by the zero memory nonlinearity approach (ZMNL), we can obtain the corresponding space-time-coding sequences for the simulation platform. To validate the proposed approach, we generate two correlated clutter signals following the Weibull and lognormal distributions under the illumination of monochromatic electromagnetic plane waves. Experimental results exhibit robust conformity with the theoretical values, demonstrating the dependability and stability of the proposed approach. These findings highlight the significant potential of our methods in various domains, including radar sensing and countermeasures.