DOA estimation based on sparse Bayesian learning under amplitude-phase error and position error

doi:10.23919/JSEE.2025.000052

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (5): 1122-1131.doi: 10.23919/JSEE.2025.000052

• ELECTRONICS TECHNOLOGY • Previous Articles

DOA estimation based on sparse Bayesian learning under amplitude-phase error and position error

Yijia DONG(), Yuanyuan XU(), Shuai LIU(), Ming JIN()

School of Information Science and Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, China

Received:2023-11-20 Accepted:2025-06-24 Online:2025-10-18 Published:2025-10-24
Contact: Shuai LIU E-mail:yijiadong99@163.com;xuyuanyuan626@163.com;liu-shuai@hit.edu.cn;jinming0987@163.com
About author:
DONG Yijia was born in 1999. He received his M.S. degree in Harbin Institute of Technology in 2023, majoring in information and communication engineering. His research interest is array signal processing. E-mail: yijiadong99@163.com

XU Yuanyuan was born in 2000. She received her M.S. degree from Harbin Institute of Technology in 2025, majoring in Information and Communication Engineering. Her research interest is array signal processing. E-mail: xuyuanyuan626@163.com

LIU Shuai was born in 1980. He received his B.S. and M.S. degrees from Northwestern Polytechnical University, Xi’an, China, in 2002 and 2005, respectively. He received his Ph.D. degree from Harbin Institute of Technology, Harbin, China, in 2013. He is a professor at Harbin Institute of Technology (Weihai). His research interests are conformal array, polarization sensitive array intelligent optimization, polarization-DOA parameter estimation method, conventional array and polarization sensitive array robust beamforming method, and radar electronic countermeasures. E-mail: liu-shuai@hit.edu.cn

JIN Ming was born in 1968. He received his B.S., M.S., and Ph.D. degrees from Harbin Institute of Technology, Harbin, China, in 1990, 1998 and 2004, respectively. He is a professor at Harbin Institute of Technology (Weihai). His research interest is radar signal processing. E-mail: jinming0987@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62071144).

Abstract

Abstract:

Most of the existing direction of arrival (DOA) estimation algorithms are applied under the assumption that the array manifold is ideal. In practical engineering applications, the existence of non-ideal conditions such as mutual coupling between array elements, array amplitude and phase errors, and array element position errors leads to defects in the array manifold, which makes the performance of the algorithm decline rapidly or even fail. In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors, this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view. In the solution, the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution. At the same time, the expectation-maximization algorithm is used to update the probability distribution parameters, and then the two error parameters are solved alternately to obtain more accurate DOA estimation results. Finally, the effectiveness of the proposed algorithm is verified by simulation and experiment.

Key words: direction of arrival estimation (DOA), amplitude and phase error, array element position error, sparse Bayesian

Yijia DONG, Yuanyuan XU, Shuai LIU, Ming JIN. DOA estimation based on sparse Bayesian learning under amplitude-phase error and position error[J]. Journal of Systems Engineering and Electronics, 2025, 36(5): 1122-1131.

Figures/Tables 7

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Symbol	Definition
${{\boldsymbol{\lambda }}_t}$	The covariance matrix of the tth snapshot data of the received data
${\boldsymbol{\xi }}$	The covariance precision parameters
$\delta $	The noise covariance accuracy
$ {\boldsymbol{d}} $	The array element position error vector
${\varepsilon _{{{\varGamma }},m}}$	The amplitude-phase error covariance accuracy
${\varepsilon _{{{d}},m}}$	The array element position error covariance accuracy
$b,c,d,e,f,h,l$	The hyperparameters of the Gamma distribution

DOA estimation based on sparse Bayesian learning under amplitude-phase error and position error

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