中文摘要：

针对方向向量偏差会导致最小均方（LMS）算法的性能急剧下降这一问题,提出了一种基于可变对角载入的顽健自适应波束形成算法。采用最陡下降法对信号方向向量进行优化求解,并在每次迭代过程中更新对角载入值,进而求出最优的权重向量,避免了矩阵求逆运算和特征值分解运算,大大降低了计算复杂度。通过建立步长与输入信号的关系得到可变的步长因子,克服了收敛速度和稳态误差之间的矛盾。该算法收敛速度快,抗扰动性强,对信号方向向量偏差具有很强的顽健性,从而改善了阵列输出的信干噪比,使其更接近最优值。理论分析和仿真结果表明与传统自适应波束形成算法相比,所提顽健算法具有更好的性能。

英文摘要：

The performance of least mean squares （LMS） algorithm degraded dramatically in the presence of even slight steering vector mismatches. In order to overcome the shortage, a novel robust adaptive beamforming algorithm based on the variable diagonal loading technique was proposed. The signal steering vector was obtained via the gradient-descent method and the diagonal loading term was incorporated at each recursive step. Then, the optimal weight vector was derived. To account for the contradiction of convergence rate and steady errors, a function between the step size and input signals was built to obtain the variable step size. The proposed algorithm had a low complexity cost without the inverse matrix and eigendecomposition. The proposed algorithm offered faster convergence rate, provided a sufficient robustness against the mismatches and made the mean output array SINR consistently close to the optimal one. The theoretical analysis and simulation results demonstrate that the performance of the proposed algorithm can outperform that of the conventional algorithm.