论文标题
urglq：一种有效的协方差矩阵重建方法，用于鲁棒自适应波束形成
URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming
论文作者
论文摘要
常规自适应波束形式的计算复杂性相对较大，并且由于模型不匹配误差和收到的数据中不需要的信号，性能大大降低。在本文中，提出了有效的不需要的信号去除和高斯 - legendre正交正交（URGLQ）基于基于的协方差矩阵重建方法。与先前的协方差矩阵重建方法不同，构建投影矩阵以从接收到的数据中删除不需要的信号，从而提高了协方差矩阵的重建精度。考虑到大多数矩阵重建算法的计算复杂性由于积分操作而相对较大，因此我们提出了一种基于高斯 - legendre正交的方法，以近似积分操作，同时保持准确性。此外，为了提高波束形式的鲁棒性，通过在校正后的转向矢量无法收敛到任何干扰转向向量的约束下，通过最大化波束形式的输出功率来纠正所需转向矢量中的不匹配。仿真结果和原型实验表明，所提出的波束形式的性能优于比较方法，并且在不同情况下更接近最佳光束形式。
The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to the model mismatch errors and the unwanted signals in received data. In this paper, an efficient unwanted signal removal and Gauss-Legendre quadrature (URGLQ)-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix reconstruction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms is relatively large due to the integral operation, we proposed a Gauss-Legendre quadrature-based method to approximate the integral operation while maintaining accuracy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiments demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.
