Novel Robust Adaptive Beamformer in the Presence of Gain-Phase Errors
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Novel Robust Adaptive Beamformer in the Presence of Gain-Phase Errors Qichao Ge1
· Yongshun Zhang2 · Ziang Feng1 · Xiangyang Liu3
Received: 21 February 2020 / Revised: 28 September 2020 / Accepted: 6 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this paper, we consider the problem of robust adaptive beamforming (RAB) for a linear array in the presence of gain-phase errors. By setting a few calibrated auxiliary elements on one side of a linear array, we propose a novel gain error and phase error estimation algorithm to calibrate gain-phase errors. According to the proposed estimation algorithm, the unknown gain error and phase error are jointly estimated by calculating the eigenvector corresponding to the minimum eigenvalue of the available transitional matrices. Furthermore, we calibrate the received data and compensate for the nonwhite noise caused by the calibration process. Then, the interference-plus-noise covariance matrix (INCM) is reconstructed based on the calibrated steering vector. By incorporating the reconstructed INCM with the minimum variance distortionless response principle, a novel robust adaptive beamformer for a linear array with gainphase errors is designed. The proposed beamformer can obviously improve RAB performance in a linear array with gain-phase errors. The robustness and superiority of the designed beamformer are demonstrated in simulations. Keywords Parameter estimation · Gain-phase errors · Calibration · Robust adaptive beamforming · MVDR principle
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Qichao Ge [email protected] Yongshun Zhang [email protected] Ziang Feng [email protected] Xiangyang Liu [email protected]
1
Graduate College, Air Force Engineering University, Xi’an 710051, China
2
Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
3
College of Information and Communication, National University of Defense Technology, Xi’an 710100, China
Circuits, Systems, and Signal Processing
1 Introduction Adaptive processing can significantly improve the practical applicability of the whole system, especially in beamforming and nonlinear system control [9, 15, 16]. As a significant component of signal processing, adaptive beamforming is widely utilized in radar, communication, sonar, medical imaging, and other fields [5, 21]. Due to its wide application, there is substantial research on adaptive beamforming. The Capon beamformer was proposed using the maximum output signal-to-interference-plus-noise ratio (SINR) principle in [2]. The standard Capon beamformer (SCB) can effectively realize beam control and interference suppression, and it also provides a foundation for future research. However, the SCB requires a desired signal steering vector and an accurate interference-plus-noise covariance matrix (INCM), so it is extremely sensitive to unsatisfactory factors, including steering vector mismatch, the presence of the desired signal, mutual coupling, gain-phase errors, etc. Furthermore, these factors seriously degrade th
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