Deep unfolded amplitude-phase error self-calibration network for DOA estimation

doi:10.23919/JSEE.2024.000099

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (2): 353-361.doi: 10.23919/JSEE.2024.000099

• ELECTRONICS TECHNOLOGY • Previous Articles

Deep unfolded amplitude-phase error self-calibration network for DOA estimation

Hangui ZHU¹(), Xixi CHEN²(), Teng MA³(), Yongliang WANG¹^,⁴^,*()

¹ School of Electronic Information, Wuhan University, Wuhan 430072, China
² Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
³ National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
⁴ Radar Weapon Application Engineering Key Research Laboratory, Air Force Early Warning Academy, Wuhan 430019, China

Received:2023-08-30 Accepted:2024-07-01 Online:2025-04-18 Published:2025-05-20
Contact: Yongliang WANG E-mail:zhg598@hotmail.com;xixichen99@163.com;tma1996@126.com;ylwangkjld@163.com
About author:
ZHU Hangui was born in 1997. He received his M.S. degree in electronics science and technology from Air Force Engineering University, Xi’an, China, in 2023. He is currently working toward his Ph.D. degree in the School of Electronic Information, Wuhan University, Wuhan, China. His current research interests include array signal processing and deep learning. E-mail: zhg598@hotmail.com

CHEN Xixi was born in 1989. She received her B.S. degree in electronic information engineering from Xiang Tan University, Xiangtan, China, in 2012, M.S. degree in information and communication engineering from Xidian University, Xi’an, China, in 2015, and Ph.D. degree in information and communication engineering at National University of Defense Technology, Changsha, China, in 2022. She is currently working at Air Force Engineering University. Her research interests include information geometry, weak target detection, and waveform design. E-mail: xixichen99@163.com

MA Teng was born in 1996. He received his M.S. degree from the College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China, in 2022. He is currently working toward his Ph.D. degree in the National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China. His research interests include multiple input multiple output radar, millimeter-wave radar technology, array signal processing, and wireless communication. E-mail: tma1996@126.com

WANG Yongliang was born in 1965. He received his Ph.D. degree in electrical engineering from Xidian University, Xi’an, China, in 1994. From June 1994 to December 1996, he was a postdoctoral fellow with the Department of Electronic Engineering, Tsinghua University, Beijing, China. He has been a full professor since 1996, and he was the director of the Key Research Laboratory, Wuhan Radar Academy, Wuhan, China, from 1997 to 2005. He is a member of the Chinese Academy of Sciences and also a Fellow of the Chinese Institute of Electronics. His recent research interests include radar systems, space-time adaptive processing, and array signal processing. E-mail: ylwangkjld@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62301598?).

Abstract

Abstract:

To tackle the challenges of intractable parameter tuning, significant computational expenditure and imprecise model-driven sparse-based direction of arrival (DOA) estimation with array error (AE), this paper proposes a deep unfolded amplitude-phase error self-calibration network. Firstly, a sparse-based DOA model with an array convex error restriction is established, which gets resolved via an alternating iterative minimization (AIM) algorithm. The algorithm is then unrolled to a deep network known as AE-AIM Network (AE-AIM-Net), where all parameters are optimized through multi-task learning using the constructed complete dataset. The results of the simulation and theoretical analysis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery methods. Furthermore, it maintains excellent estimation performance even in the presence of array magnitude-phase errors.

Key words: direction of arrival (DOA), sparse recovery, alternating iterative minimization (AIM), deep unfolding, amplitude-phase error

Hangui ZHU, Xixi CHEN, Teng MA, Yongliang WANG. Deep unfolded amplitude-phase error self-calibration network for DOA estimation[J]. Journal of Systems Engineering and Electronics, 2025, 36(2): 353-361.

Figures/Tables 6

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References 30

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Deep unfolded amplitude-phase error self-calibration network for DOA estimation

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