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

ABSTRACT

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.

KEYWORDS:

• Faulty elements diagnosis
• phased array antennas
• generative adversarial learning
• stacked denoising sparse autoencoder

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Da Lin

Da Lin received the PhD degree in communication and information system from Wuhan University, Wuhan, China, in 2016. He is currently working in the 722 Research Institute of CSIC, Wuhan, China. His research interests include computational electromagnetics, antenna beamforming, phased array antenna fault diagnosis, and machine learning with applications to antenna diagnosis.

Qi Wan

Qi Wan received the MS degree in Wuhan Research Institute of Posts and Telecommunications, Wuhan, China, in 2017. She is currently working in Accelink Technologies Co. Ltd., Wuhan, China. Her research interests include development of intelligent optical devices and systems, and component fault detection and isolation.