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High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections

机译:存在阵列缺陷的高性能鲁棒自适应波束成形

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摘要

A high performance robust beamforming scheme is proposed to combat model mismatch. Our method lies in the novel construction of interference-plus-noise (IPN) covariance matrix. The IPN covariance matrix consists of two parts. The first part is obtained by utilizing the Capon spectrum estimator integrated over a region separated from the direction of the desired signal and the second part is acquired by removing the desired signal component from the sample covariance matrix. Then a weighted summation of these two parts is utilized to reconstruct the IPN matrix. Moreover, a steering vector estimation method based on orthogonal constraint is also proposed. In this method, the presumed steering vector is corrected via orthogonal constraint under the condition where the estimation does not converge to any of the interference steering vectors. To further improve the proposed method in low signal-to-noise ratio (SNR), a hybrid method is proposed by incorporating the diagonal loading method into the IPN matrix reconstruction. Finally, various simulations are performed to demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches. The output signal-to-interference-plus-noise ratio (SINR) improvement of the beamformer due to the proposed method is significant.
机译:提出了一种高性能的鲁棒波束成形方案来解决模型失配问题。我们的方法在于干扰加噪声(IPN)协方差矩阵的新颖构造。 IPN协方差矩阵由两部分组成。第一部分是通过利用在与所需信号方向分开的区域上积分的Capon频谱估计器获得的,而第二部分是通过从样本协方差矩阵中删除所需信号分量而获得的。然后,利用这两部分的加权求和来重建IPN矩阵。此外,还提出了一种基于正交约束的转向矢量估计方法。在该方法中,在估计不收敛到任何干扰导引向量的情况下,通过正交约束校正假定的导引向量。为了在低信噪比(SNR)下进一步改进该方法,提出了一种将对角线加载方法合并到IPN矩阵重构中的混合方法。最后,进行各种仿真以证明所提出的波束形成器针对各种阵列失配具有强大的鲁棒性。所提出的方法对波束形成器的输出信噪比(SINR)的改善意义重大。

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  • 来源
    《International journal of antennas and propagation》 |2016年第2期|3743509.1-3743509.12|共12页
  • 作者单位

    Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Peoples R China;

    Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Peoples R China;

    Naval Acad Armament, Beijing 100161, Peoples R China;

    Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Peoples R China;

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