Radiation Pattern Recovery of Deformed Microstrip Array Antenna Based on Deep Learning Method

A deep learning-based method for the radiation pattern recovery of deformed microstrip array antennas is proposed. The proposed network is developed to take the central coordinates of deformed antenna elements as input, and outputs the amplitudes and phases. The amplitudes and phases are used as excitations to recover the antenna's radiation pattern. A 1 × 7 microstrip patch array antenna is utilized for validating the correctness of the proposed network. Consequently, the recovery of the radiation pattern is highly efficient, with the main lobe beam essentially recovered to its original radiation pattern, achieving recovery speeds in the range of seconds. The results highlight the potential of the proposed method for precise and rapid recovery of the radiation pattern of deformed array antennas in practical applications.