Simplified eignspace-based robust adaptive beamforming algorithm
一种简化特征空间稳健自适应波束形成算法*

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.