A novel adaptive beamforming scheme for array signal data processing

EURASIP Journal on Wireless Communications and Networking

Research

Heping Shi¹  Guanghui Yan¹  Haijing Hou¹  Xiaoheng Jiang² 

[1] School of Automobile and Transportation, Tianjin University of Technology and Education, 300222, Tianjin, China;School of Information Engineering, Zhengzhou University, 450001, Zhengzhou, Henan, China;

关键词: Data processing;    Array signal processing;    Interference-plus-noise space (INS);    Desired signal cancelation;   

DOI  :  10.1186/s13638-023-02298-5

 received in 2023-07-07, accepted in 2023-08-22,  发布年份 2023

来源: Springer

【 摘 要 】

When the desired signal data exists in the array received data or the steering vector has a mismatch problem, the current traditional adaptive beamformers will suffer from the effect of the desired signal cancelation phenomenon, resulting in a sharp decline in performance. To address the occurrence of desired signal cancelation, an improved matrix projection-based efficient beamforming method is proposed. Firstly, based on spatial partitioning (SP) technology, a significant projection matrix for interference-plus-noise space (INS) is constructed. Secondly, using the constructed key projection matrix, the sample data covariance matrix is projected into the INS to achieve the goal of suppressing the desired signal data information. Finally, the weight data vector is calculated by Capon beamformer. The proposed algorithm does not require an iterative search for the optimal solution, which has the advantage of a small amount of calculation. Simulation experiments have verified that the proposed method has significant advantages in suppressing the desired data signals. Especially when the desired data signal has large power, the signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm is better than that of the compared algorithms under the conditions of random directionality errors or local scattering errors between the desired signal and interference.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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