A robust beamforming algorithm based on double-layer reconstruction of covariance matrix
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摘要:
针对协方差矩阵含目标信号分量及目标导向矢量失配情况下，传统自适应波束形成器性能急剧下降的问题，提出了干扰加噪声协方差矩阵双层重构的稳健波束形成算法。首先，利用稀疏重构的方法预估干扰加噪声协方差矩阵，通过估计干扰导向矢量及干扰功率对干扰加噪声协方差矩阵进行优化校正；然后，基于子空间理论建立导向矢量约束误差优化模型，利用迭代方法对凸优化模型进行求解，得到最优权值向量。仿真结果表明：所提算法显著提高了波束形成器在目标导向矢量约束误差及阵列误差情况下的稳健性，低快拍条件下表现较好，输出性能优于仿真对比算法。
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关键词:
- 自适应波束形成 /
- 干扰与噪声协方差矩阵 /
- 双层重构 /
- 导向矢量估计 /
- 迭代求解
Abstract:Focusing on the problem that the performance of traditional adaptive beamformer declines sharply when the covariance matrix contains the target signal component and the target steering vector mismatch, a robust beamforming algorithm based on double-layer reconstruction of interference-plus-noise covariance matrix is proposed in this paper. Firstly, sparse reconstruction method is used to estimate interference-plus-noise covariance matrix. The interference-plus-noise covariance matrix is optimized by estimating the interference steering vector and interference power. Secondly, based on subspace theory, an optimization model of steering vector constraint error is established, and the convex optimization model is solved by iterative method to obtain the optimal weight vector. The simulation results show that the proposed algorithm improves the robustness of the beamformer in the case of target vector constraint error and array error. The algorithm performs well in low-speed snapshot and its output performance is better than the existing methods.
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图 2 目标来波方向误差下不同算法平均输出SINR随输入SNR变化
Figure 2. SINR versus SNR for different algorithms under direction error of target signal
图 3 目标来波方向误差下不同算法平均输出SINR随快拍数变化
Figure 3. SINR versus snapshot number for different algorithms under direction error of target signal
图 4 阵列位置误差下不同算法平均输出SINR随输入SNR变化
Figure 4. SINR versus SNR for different algorithms under array position errors
图 5 阵列位置误差下不同算法平均输出SINR随快拍数变化
Figure 5. SINR versus snapshot number for different algorithms under array position errors
图 6 阵列互耦误差下不同算法平均输出SINR随输入SNR变化
Figure 6. SINR versus SNR for different algorithms under array mutual coupling errors
图 7 阵列互耦误差下不同算法平均输出SINR随快拍数变化
Figure 7. SINR versus snapshot number for different algorithms under array mutual coupling errors
图 9 松弛因子对算法收敛速度影响
Figure 9. Influence of relaxation factor on convergence speed of algorithm
表 1 收敛速度统计
Table 1. Convergence rate statistics
Tmax 运行时间/ms γ=10-2 γ=10-3 γ=10-4 γ=10-5 γ=10-6 20 71.4 91.1 121.8 30 159.6 40 243.3 
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