Robust Adaptive Beamforming Algorithm Based on Dominant Eigenvector Extraction and Orthogonal Projection

LIU Yiyuan; ZHANG Xiaokai; XU Yuhua; ZHENG Xueqiang; YANG Weiwei

doi:10.11999/JEIT251282

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LIU Yiyuan, ZHANG Xiaokai, XU Yuhua, ZHENG Xueqiang, YANG Weiwei. Robust Adaptive Beamforming Algorithm Based on Dominant Eigenvector Extraction and Orthogonal Projection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251282

Citation:

LIU Yiyuan, ZHANG Xiaokai, XU Yuhua, ZHENG Xueqiang, YANG Weiwei. Robust Adaptive Beamforming Algorithm Based on Dominant Eigenvector Extraction and Orthogonal Projection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251282

Citation:

LIU Yiyuan, ZHANG Xiaokai, XU Yuhua, ZHENG Xueqiang, YANG Weiwei. Robust Adaptive Beamforming Algorithm Based on Dominant Eigenvector Extraction and Orthogonal Projection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251282

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Robust Adaptive Beamforming Algorithm Based on Dominant Eigenvector Extraction and Orthogonal Projection

doi: 10.11999/JEIT251282 cstr: 32379.14.JEIT251282

College of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China

Funds: The National Natural Science Foundation of China (6240013161, 62327802), The Natural Science Foundation of Jiangsu (BK20241601)

Received Date: 2025-12-03
Accepted Date: 2026-01-06
Rev Recd Date: 2026-01-05

Available Online: 2026-01-12

Abstract

Abstract

Objective In practical applications, the spatial anti-jamming performance of adaptive beamformers is often degraded by mismatches in the Directions Of Arrival (DOAs) of signals. Some robust adaptive beamforming algorithms reduce the error between the estimated signal steering vector and the actual steering vector by solving a Quadratic Constrained Quadratic Programming (QCQP) problem. This strategy significantly increases hardware cost. In addition, traditional adaptive beamforming algorithms often exhibit beampattern distortion under non-ideal conditions, such as DOA mismatch. The objective of this paper is to design a robust adaptive beamformer that effectively suppresses jamming signals under different mismatch scenarios. Methods A robust adaptive beamforming algorithm for spatial anti-jamming is proposed. First, the actual output Signal-to-Jamming-plus-Noise Ratio (SJNR) in the presence of DOA mismatch is analyzed. An ideal beamformer based on orthogonal projection is then proposed to achieve accurate beampattern control and maximize the practical output SJNR. To improve anti-jamming robustness in mismatch environments, the signal steering vector is estimated through covariance matrix construction and dominant eigenvector extraction. The beamforming weight vector is obtained by constructing an orthogonal projection matrix. Results and Discussions The proposed adaptive beamforming algorithm effectively suppresses jamming signals in mismatch environments. Numerical results show that the algorithm achieves good spatial anti-jamming performance in an ideal scenario without mismatch (Fig. 3) and in a scenario with steering vector mismatch (Fig. 4). In DOA mismatch scenarios, the proposed algorithm demonstrates superior beampattern performance (Fig. 5, Fig. 6) and output SJNR performance (Fig. 7, Fig. 8, Fig. 9). The results also indicate stronger robustness to DOA mismatch (Fig. 10, Fig. 11). Effective jamming suppression is maintained even when the incoming directions of the jamming signals are closely spaced (Fig. 12). Conclusions This paper proposes a robust adaptive beamforming algorithm for suppressing power suppressive jamming signals. An ideal beamformer is first developed to achieve precise beampattern control and maximize the actual output SJNR. A robust adaptive beamforming algorithm is then constructed through covariance matrix construction, dominant eigenvector extraction, and orthogonal projection. Numerical results show that the proposed algorithm provides strong spatial anti-jamming performance in ideal scenarios without mismatch and in scenarios with DOA mismatch or steering vector mismatch.
- Robust adaptive beamforming,
- Dominant eigenvector extraction,
- Steering vector estimation,
- Orthogonal projection

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