一种简化特征空间稳健自适应波束形成算法*

在基于特征空间(ESB)的自适应波束形成算法中，针对当指向误差落在波束主瓣的边缘特定角度时，输出信干噪比下降，且信号子空间需要进行费时的特征值分解的问题，提出了改进线性约束最小方差(LCMV)算法。在假定的期望信号方向附近减少一个方向性约束条件，并基于信号特征值大于噪声特征值的这一特性, 利用空间协方差矩阵逆的高阶次幂来逼近信号子空间，无须特征分解，将求得的权矢量向改进的信号子空间投影。该方法能够大大减少计算量，同时还显著提高了自适应波束形成稳健性。通过仿真分析及结果比较验证了算法的正确性和有效性，因此从

Simplified eignspace-based robust adaptive beamforming algorithm

In the eignspace-based(ESB) adaptive beamforming algorithm, when the pointing error falls in some certain positions near the mainlobe edge, the output SINR would reduce and for the signal subspace, the time-consuming eigenvalue decomposition would be needed. This paper put forward the improved linear constraints minimum variance (LCMV) algorithm, which would reduce a directional constraint near the direction of the assumed desired signals. And through the high order power of the inverse spatial covariance matrix to approach the signal subspace based on the characteristic of signal eigenvalue larger than noise eigenvalue which could avoid the eigen decomposition, then projected the obtained weight vector onto the improved signal subspace. Compared by simulation analysis and results show that the method can greatly reduce the computation, but also significantly improve the robustness of adaptive beamforming. Therefore, from the engineering application perspective, the research is of great reference value.