一种分解迭代二阶锥规划鲁棒自适应波束形成算法

为有效克服导向矢量大失配误差对自适应波束形成器的影响，该文提出了一种最差性能最优的分解迭代鲁棒自适应波束形成算法。该算法对非凸的幅度响应约束问题进行分解处理，将问题转化为迭代的二阶锥规划问题，从而可对鲁棒响应区的波束宽度和纹波水平进行自由控制，并可得到较高的输出信干噪比。此外，与现有大部分该类鲁棒波束形成方法相比，提出的算法直接对权矢量进行优化，无需使用谱分解算法，避免了阵列结构的限制，可适用于任意阵形。仿真结果验证了算法的正确性和有效性。

A Robust Adaptive Beamforming Algorithm Using Decomposition and Iterative Second-order Cone Programming

To overcome effectively the influence of large steering vector mismatch on the performance of adaptive beamformer,a Robust Adaptive Beamformer using Decomposition and Iterative Second-Order Cone Programming via Worst-Case performance optimization(RAB-DISOCP-WC) is proposed in this paper.Due to the decomposition and iterative method for the non-convex magnitude response constraints,the problem can be optimally solved using iterative Second-Order Cone Programming(SOCP),then the beamwidth and ripple of the robust response region can be flexibly controlled by the proposed method,and the output Signal-to-Interference-and-Noise Ratio(SINR) can be obviously improved.Moreover,in constrast to most of this class of robust beamformers,the proposed approach can get the optimal weight vector directly,and it does not need any spectral factorization.Thus,the proposed approach does not have any array geometry constraint,and it is applicable to arbitrary array geometries.Simulation results verify the correctness and validity of the proposed approach.