Model calibration for compressive sensing based linear antenna array fault diagnosis

ABSTRACT

The Compressive Sensing (CS) theory has been widely studied as it requires only a small number of measurements to identify faulty elements of antenna arrays. However, the model for linear antenna array fault diagnosis based on CS is much sensitive to model mismatch and the identification results usually turn out to be much worse than expected in the practical situation. In this study, the model mismatch for linear antenna array fault diagnosis is first analyzed and then a CS model calibration method is proposed to improve the performance of the CS-based linear antenna array fault diagnostic method by solving a convex optimization model. Numerical results demonstrate that the calibrated CS model is much more accurate in identifying faulty antenna elements under model mismatch and noise than the traditional one.

KEYWORDS:

• Linear antenna array
• compressive sensing
• model mismatch
• model calibration
• fault diagnosis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Weirui Chen

Weirui Chen is currently pursuing the Ph.D. degree in control science and engineering with the School of Automation, Southeast University, China. He received the B.S. degree in automation from Fuzhou University, Fuzhou, China, in 2019, and the M.S. degree in control science and engineering from Southeast University, Nanjing, China, in 2021. His research interests include applied electromagnetics, machine learning and optimization, and modelling and controlling of the radio telescope.

Xiangwen Yang

Xiangwen Yang is currently pursuing his M.S. degree at Southeast University in Nanjing, China. He received his Bachelor of Engineering degree from Chongqing University, Chongqing, China. His research interests include data-driven and model-driven modeling techniques, and machine learning and optimization.

Dongyang Li

Dongyang Li is a graduate student in the School of Automation at Southeast University, Nanjing, China, focusing on control science and engineering. His research interests include Intelligent transportation, manipulator control, object detection, and intelligent algorithm and optimization.

Jian Li

Jian Li is a graduate student in the School of Automation at Southeast University, Nanjing, China, focusing on control science and engineering. His research interests include electromagnetics, object detection, and intelligent algorithm and optimization.