Electromagnetic-Model-Driven Twin Delayed Deep Deterministic Policy Gradient Algorithm for Stealthy Conformal Array Antenna
Binchao Zhang, Cheng Jin, Buning Tian, Kaiqi Cao, Junwei Wang, Pengyu Zhang
Abstract
Conformal array antennas (CAAs) can effectively broaden the beam scanning range and improve radiation flexibility. Unfortunately, confirmation will increase the difficulty of analysis and synthesis, and the stealth of CAAs is arduous. In this article, we propose an innovative array optimization algorithm for stealthy CAAs based on the twin delayed deep deterministic policy gradient (TD3) algorithm, which combines the advantages of deep learning and reinforcement learning. Furthermore, we characterize the CAA as an electromagnetic model (EMM) and embed it into the TD3 algorithm as the environment to improve its stability and efficiency. Additionally, we independently excite the radiation and absorption modes to integrate radiation and stealth characteristics into the CAA. The simulated and measured results indicate that the designed CAA with EMM-TD3 algorithm features high-flexibility beam scanning within ±50° around 6 GHz and 10 dB radar cross section reduction in the frequency bands of 3.1–5.3 and 6.5–11.2 GHz.