Element Failure Diagnosis and Pattern Recovery for Array Antennas

To address the issues of element failures and performance degradation in antenna arrays caused by harsh outdoor environments, in this paper, we propose a neural network-based fault diagnosis method alongside an adaptive-threshold radiation pattern recovery algorithm based on the Fast Fourier Transform (FFT). For fault diagnosis, the proposed method utilizes the far-field patterns of the damaged array as input data to train and test a Convolutional Neural Network (CNN). The training dataset comprises simulated data embedded with Gaussian noise to replicate real-world conditions. Distinguishing itself from conventional neural network-based diagnostic approaches, this method innovatively applies sum-and-difference beam processing to the far-field data, effectively resolving the long-standing challenge of diagnosing symmetrical element failures while maintaining low computational complexity and achieving superior identification accuracy. Regarding performance recovery, an adaptive threshold mechanism is integrated into the traditional iterative FFT optimization algorithm. This mechanism dynamically adjusts the restoration criteria according to diverse element failure scenarios and significantly reduces the number of iterations, thereby lowering the computational overhead while enhancing the efficiency of radiation pattern recovery.