Beam Stabilization of Deformed Conformal Array Antenna Based on Physical- Method -Driven Deep Learning

The array antenna will be inevitably deformed affected by the motion of the communication system or other special requirements, which will reduce the original good radiation performance, such as gain dropping, sidelobe increasing, and even beam splitting. To cope with the beam deterioration caused by the array antenna's deformation, it is necessary to quickly manipulate the amplitude and phase distribution according to the current conformal array shape to stabilize the beam. This article proposes a physical-method-driven deep-learning-based (PMDL-based) fast beam stabilization algorithm for antenna array deformation. First, we theoretically analyze the radiation pattern synthesis of the conformal array with the arbitrary surface to design the corresponding physical method, and we verify the accuracy of the method by the calculated and simulated results. Then, the deep neural network (DNN) driven by the physical method is designed integrated with the aforementioned radiation pattern synthesis, whose training process is given. Finally, a $1\times16$ array antenna is taken as an example to verify the validity of the beam stabilization algorithm when the designed array is deformed. The simulated and measured results indicate that gain dropping and sidelobe level (SLL) rising are, respectively, less than 1.5 and 2 dB by applying the proposed PMDL-based fast beam stabilization when the array antenna is deformed within the range of ${\mathrm {100}}^{\circ }$ of the normal vector of each array element, while the beam stabilization time is less than 1.0 ms (testing time).