Antenna Array Diagnosis Using a Deep Learning Approach

Abstract

In this communication, we propose to use a deep learning (DL) approach to detect unit failure in array antennas. Due to natural machine life cycle and/or unexpected accidents, antenna units unavoidably suffer from the risk of failure, leading to the deterioration of array performance. To realize the detection of unit failure, the far-field radiation patterns are used as the input of the deep convolutional neural network (DConvNet) for antenna array diagnosis learning. The proposed DConvNet consists of continuous functional groups of convolution, batch normalization, and activation layers, followed by a fully connected layer to realize recognition, i.e., the fault diagnosis of antenna array. Different from conventional diagnosis techniques, the main advantage of the proposed DL approach does not require intensive computations based on Green's function. The training data are collected by the electromagnetic (EM) simulation tool. Additionally, the Gaussian noise is added to the training data to imitate the interference in real application scenarios. The proposed DConvNet for array diagnosis is verified by three numerical benchmarks and demonstrates that it can diagnose antenna array in a complex environment with generality.