Traditional backscatter enhancement devices include corner reflectors and Luneburg lenses. Corner reflectors are typically made of metal and consist of two or three angled surfaces, resulting in heavy weight and large volume. Luneburg lenses are made of dielectric materials and have higher costs. Both devices require a certain amount of space and are not easy to place. To address these issues, this paper presents a 1-bit 12x12 reconfigurable reflection array that can be attached to the surface of smooth objects. It operates in the frequency range of 10.5-11.5 GHz and achieves full coverage of high-gain backscattering radar cross section (RCS) within the -45°to 45°angular range by changing the coding arrangement of the array. The 1-bit reconfigurable unit is composed of four basic units, with each unit having a PIN diode connecting two separate square metal plates on its surface. By controlling the on/off state of the PIN diodes, a phase difference of 180°in the reflected electromagnetic waves is achieved, enabling the 0/1 coding of the array. The combination of the deep deterministic policy gradient (DDPG) algorithm and electromagnetic simulation software is employed to search for coding schemes with high gain, directly evaluating the simulation environment helps to avoid errors that may arise from theoretical calculations and simulations. Simulation results show that, the designed metasurface achieves 5~27dB RCS enhancement for y-polarized waves within the 90°angular range, demonstrating superior wide-angle RCS enhancement effects.
Metasurface is a new type of electromagnetic material based on the generalized Snell theorem . It can achieve different reflection and incidence effects from natural materials, which has attracted extensive attention in the field of electromagnetics. In this paper, a one-dimensional metasurface is designed and simulated which can change the reflected electromagnetic wave beam. The additional phase difference on the metasurface unit is controlled by 1 bit coding to control the direction of the reflected wave beam. Combined with the Deep Q Network algorithm in the reinforcement learning tool, the coding corresponding to the beam meeting the target direction is found.
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