Failure correction of linear antenna array using complex method enhanced teaching-learning based optimization algorithm

ABSTRACT

The relatively high probability of elements failure is a concerning problem for large scale phased antenna arrays, which leads to degrade the antenna’s performance significantly. The array performance degradation can be corrected by re-optimizing the array excitations. Teaching-learning based optimization (TLBO) is newly proposed swarm intelligence based evolutionary algorithm, which imitates the social behavior of teaching-learning process in the classroom. In this paper, an effective approach using complex method enhanced teaching-learning based optimization (ETLBO) algorithm is proposed for correction of antenna arrays in the presence of faulty elements. Numerical simulation results show that the proposed ETLBO algorithm having more advantages in finding global optimal solution than original TLBO algorithms. Experimental results of a 32-element phased array are presented to demonstrate that the proposed ETLBO algorithm is effective in practical engineering field.

KEYWORDS:

• Array failure correction
• evolutionary algorithms
• teaching-learning based optimization
• linear antenna array

Acknowledgments

The authors would like to thank Dr. Yao Bin for providing phased array imaging experimental data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported partly by the National Natural Science Foundation of China under [grant number 41906169] and [grant number 51705163], and partly by the Zhejiang Provincial Natural Science Foundation under [grant number LY15E090004].

Notes on contributors

Zailei Luo

Zailei Luo received his BE and PhD degrees in Mechanical Engineering and Mechatronics from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2012 and 2018, respectively. He is currently a research assistant professor with Advanced Interdisciplinary Technology Research Center, National Innovation Institute of Defense Technology, Beijing, China. His research interests include array signal processing and sonar imaging technology.

Bin Han

Bin Han received the BE and PhD degrees in mechanical engineering and mechatronics from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2008 and 2013, respectively. From 2014 to 2016, he joined the department of Automation at Tsinghua University as a Postdoctoral Fellow. He is currently a lecturer with the State Key Laboratory of Digital Manufacturing Equipment and Technology, HUST. His research interests include array signal processing and underwater robots.

Xueming He

Xueming He received his BE degree in Mechanical Engineering and Mechatronics from National University of Defense Technology (NUDT), Changsha, China, in 1999, and PhD degree in Mechanical Engineering and Mechatronics from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2007. He is currently a professor in China Coast Guard Academy, Ningbo, China. His research interests include underwater vehicle technology, underwater anti-terrorism technology, sparse array and sonar imaging technology.

Dexin Zhao

Dexin Zhao received his BE and PhD degrees in Instrument Science and Technology from National University of Defense Technology (NUDT), Hunan, China, in 2006 and 2013, respectively. He is currently a research assistant professor with Advanced Interdisciplinary Technology Research Center, National Innovation Institute of Defense Technology, Beijing, China. His research interests include array signal processing, machine learning and underwater sensing technology.