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首页> 外文期刊>Signal processing >Robust adaptive beamforming via a novel subspace method for interference covariance matrix reconstruction
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Robust adaptive beamforming via a novel subspace method for interference covariance matrix reconstruction

机译：通过一种新颖的子空间方法进行鲁棒的自适应波束成形，以重建干扰协方差矩阵

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摘要

In this paper, a novel subspace method is proposed to reconstruct the interference-plus-noise covariance matrix (IPNCM) according to its definition, which can fundamentally eliminate the signal of interest (SOI) component from the sample covariance matrix (SCM). The central ideal is that each interference steering vector (SV) is estimated by the vector lying within the intersection of two subspaces while its power obtained by using the Capon spectral estimator. The first subspace is the interference subspace and it is obtained from each interference covariance matrix term calculated by integrating over each interference angular sector. The second one is the signal-interference subspace got from the SCM. Then a more precise IPNCM is reconstructed based on these accurate estimations. Meanwhile the signal covariance matrix is calculated by integrating over the SOI angular sector so that a new SV estimation of SOI can be obtained from its prime eigenvector. Finally, based on the new IPNCM and the SV of SOI, a novel robust beam-former is formulated to improve the robustness against array model mismatches. Simulation results demonstrate that the proposed beamformer outperforms other existing reconstruction-based beamfor-mers and almost attains the optimal performance in both low and high input signal-to-noise ratio (SNR) cases.
机译：本文提出了一种新的子空间方法，根据其定义重建干扰加噪声协方差矩阵（IPNCM），可以从样本协方差矩阵（SCM）根本上消除感兴趣信号（SOI）分量。中心的理想是，每个干扰控制向量（SV）都由位于两个子空间相交处的向量估计，而其功率是通过使用Capon频谱估计器获得的。第一子空间是干扰子空间，它是通过对每个干扰角扇区进行积分而计算出的每个干扰协方差矩阵项获得的。第二个是从单片机获得的信号干扰子空间。然后，基于这些准确的估计值，将重建更精确的IPNCM。同时，通过在SOI角扇区上积分来计算信号协方差矩阵，以便可以从其原始特征向量获得SOI的新SV估计。最后，基于新的IPNCM和SOI的SV，制定了一种新颖的鲁棒波束形成器，以提高针对阵列模型不匹配的鲁棒性。仿真结果表明，所提出的波束形成器性能优于其他现有的基于重构的波束形成器，并且在低和高输入信噪比（SNR）情况下几乎都能达到最佳性能。

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