基于二次型约束的鲁棒自适应波束形成算法

针对期望信号方向向量存在偏差会导致自适应波束形成算法的性能急剧下降这一问题，该文提出了一种基于二次型约束的鲁棒自适应波束形成算法。通过对期望信号波达方向附近范围内的方向向量的误差模值进行约束，来提高算法的鲁棒性，并在此约束条件下对权重向量进行优化求解，且优化解中的参数能够准确求出。该算法可有效地控制波束主瓣区域内信号的畸变，并能够抑制方向向量偏差所带来的影响，提高了系统的鲁棒性，同时使干扰和噪声的功率输出最小，保证了对干扰信号的抑制能力，改善了阵列输出的信干噪比，使其更接近最优值。仿真结果验证了所提算法的有效性与优越性。

Robust Adaptive Beamforming Algorithm with a Quadratic Constraint

The performance of adaptive beamforming algorithms is known to degrade severely in the presence of even slight signal steering vector mismatches. To account for the mismatches, a novel robust adaptive beamforming algorithm is proposed. To improve robustness, the weight vector is optimized to involve minimization of a quadratic function subject to the norm of error between the actual and assumed signal steering vectors and the parameter in the optimal solution can be solved accurately. The proposed algorithm can suffer the least distortion from the directions near the steering angle, reduce the influence of the mismatches, and provide an improved robustness against the mismatches. Moreover, it can suppress the interference signals and make the mean output array SINR consistently close to the optimal one. Simulation results demonstrate its validity and superiority as compared with the conventional algorithms.