基于MNC-FastICA算法的稳健自适应波束形成

针对导向向量失配的稳健自适应算法主要是基于导向向量不确定集约束，但是其约束参数往往难以确定，提出了基于修正的非圆复值快速不动点算法（MNC-FastICA）的波束形成方法，通过盲分离得到的分离矩阵来构造波束权向量，并对由此产生的信源幅相模糊进行校正。该方法不必预先估计信号来波方向，避免了传统方法中来波方向估计不准引起的期望信号的导向向量失配；对于阵列通道幅相误差导致的导向向量失配，该方法对其不敏感，不必对阵列进行校正。仿真实验与最差性能最佳化（WCPO）等经典方法作了性能对比，结果验证了该算法的有效性和稳健性。

Robust Adaptive Beamforming Based on MNC-FastICA Algorithm

The actual steering vector with errors is usually modeled as an uncertainty set in most robust adaptive beamforming (RAB) against the steering vector mismatch. However, it is commonly difficult to determine the constraint parameter in practice. In this paper, a RAB based on modified noncircular complex fast independent component analysis (MNC-FastICA) algorithm is proposed. The weight vector of beamformer can be constructed with the separation matrix found by MNC-FastICA algorithm and the amplitude and phase ambiguities of estimations resulted from separation are also calibrated. Thus, the signal directions of arrival (DOA) do not need to be predestinated, which voids the mismatch of signal steering vector due to the error of DOA in classical RAB methods. Moreover, the proposed method is not sensitive to the amplitude and phase errors of array channel so that array calibration is not necessary. Simulations are run and the performances are compared with classical methods such as worst-case performance optimization (WCPO). Results demonstrate the effectiveness and robustness of our method.