A Novel AI Model for Optimizing Excitation Parameters in Irregular Phased Arrays

For irregular phased arrays, the failure of certain array elements due to uncontrollable factors inevitably leads to varying degrees of degradation in beam pattern performance. Restoring or improving the beam pattern performance thus becomes an unavoidable issue. The most direct approach to addressing this problem is to compensate for beam pattern defects by reasonably adjusting the excitation parameters of the remaining functional elements.Since this study considers scenarios where any array element may fail, with the goal of rapidly compensating for beam pattern performance under arbitrary failure conditions, the complexity of irregular phased array cases and the stringent real-time requirements make traditional adaptive array algorithms inadequate. Therefore, this research explores the use of artificial intelligence (AI) methods, leveraging the model’s generalization capability and superior computational power, which holds promise for solving such highly real-time demanding problems.