Faulty elements diagnosis of phased array antennas using a generative adversarial learning-based stacked denoising sparse autoencoder

Diagnosis of faulty elements in a linear phased array antenna is of great importance in the wireless communication field which has been received increasing attention. As a result of element or elements failure in the linear phased array antennas, the whole radiation pattern will suffer from high side lobe levels, wide bandwidth and unexpected nulls. To this end, we suggest a novel approach by combining the generative adversarial learning and the stacked denoising sparse autoencoder to determine the location of the faulty elements in antennas. The suggested approach can learn discriminative features from radiation pattern images adaptively and automatically with less expert knowledge. Meanwhile, the suggested approach is able to overcome the strong noise, the high dimensional size of the radiation pattern and the small fault samples. In this regard, the suggested approach possesses superiority in discriminant capability in contrast to the existing related approaches.