基于信号子空间重构的鲁棒子区域Frost波束形成

本文针对一个感兴趣子区域内的宽带波束形成问题, 提出一种基于信号子空间重构的鲁棒Frost波束形成算法. 该算法的基本思想是利用一个矩阵滤波器从所估计的信号加干扰子空间中提取感兴趣信号(SOI)的特征分量; 然后利用该特征分量重构信号子空间; 最后利用重构的信号子空间构造一组线性约束最小方差(LCMV)准则, 来保证SOI近似无失真通过. 与现有的其他鲁棒Frost波束形成算法相比, 本文算法的一个显著优点是在未知SOI实际波达方向与频带等先验信息的情况下, 其导向与带宽均能够自适应地匹配SOI. 因此在整个感兴趣子区域内, 它都能获得接近最优值的输出信干噪比. 理论分析与仿真研究验证了算法的有效性.

Robust sub-regional Frost beamforming based on the signal subspace reconstruction

For the problem of broadband beamforming in a subregion of interest, a robust Frost beamforming algorithm is derived by reconstructing the signal subspace. The basic idea of the proposed algorithm is to extract the characteristic components of the signal of interest (SOI) from the estimated signal-plus-interference subspace by a matrix filter first, then employ these characteristic components to reconstruct the signal subspace, and finally construct a set of linearly constrained minimum variance (LCMV) constraints to protect the SOI components. Compared with some other robust Frost beamformers, the proposed algorithm has a significant advantage, i.e., its steering-angle and band are effective to match the SOI without prior information. Hence, the performance of the proposed algorithm is almost always close to the optimal value across the whole region of interest. Theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.