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An RCB-Like Steering Vector Estimation Method Based on Interference Matrix Reduction

机译：基于干扰矩阵减少的RCB样转向载体估计方法

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摘要

To develop an adaptive beamformer against the signal of interest (SOI) steering vector mismatch, a robust Capon beamformer (RCB) like steering vector estimation method based on the interference matrix reduction is proposed. Different from the RCB and its modified versions that optimize the SOI steering vector with the Capon power estimator, this article designs an SOI power estimator to formulate the steering vector optimization problem with an uncertainty set constraint. In terms of that, the unknown SOI covariance matrix is needed to realize the SOI power estimator, an efficient interference matrix reconstruction way via SOI blocking and matrix eigen-transition is exploited to reduce the interference component from the sample covariance matrix. Herein, after solving the given steering vector optimization problem and adding the noise component to the aforesaid interference matrix, the weight vector of the derived algorithm is, thereby, computed using the estimated SOI steering vector and interference covariance matrix. The proposed method only requires the source number and prior direction of the SOI. The numerical simulations show that the proposed approach can outperform the compared ones with reduced complexity in the situation of various steering vector mismatches.
机译：为了开发用于感兴趣的信号（SOI）转向载体错配的自适应波束形成器，提出了一种基于干扰矩阵减少的转向载体估计方法的强大的Capon波束形成器（RCB）。本文设计了利用CAPON电源估计器优化SOI转向向量的RCB及其修改版本，设计了SOI功率估计器，以通过不确定性集约束制定转向载体优化问题。就此而言，需要利用SOI阻塞和矩阵矩阵转换的所未知的SOI协方差矩阵来实现SOI功率估计器，以降低来自样本协方差矩阵的干扰分量。这里，在求解给定的转向矢量优化问题并将噪声分量添加到上述干扰矩阵之后，因此，使用估计的SOI转向向量和干扰协方差矩阵来计算派生算法的权重向量。所提出的方法仅需要SOI的源码和先前方向。数值模拟表明，所提出的方法可以优于与各种转向载体不匹配的情况降低复杂性的比较。

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