文档介绍：摘要
在实际的自适应波束形成系统中，导向矢量失配是不可避免的，此时 Capon
波束形成器的性能大大下降。因此，研究具有鲁棒性的自适应波束形成算法具有
重大意义。虽然不确定集类的鲁棒算法可以抵抗导向矢量失配，但其性能在失配
误差较大时并不好。本文提出了一种基于误差估计的最差性能最优迭代算法，其
主要内容包包含以下几点：
1．迭代地使用最差性能最优算法更新权矢量，在每步迭代中利用较小地不确
定集约束，提高了输出信干噪比；
2．针对不同信噪比下算法的收敛状况设置不同的迭代终止条件，并通过最大
化化输出信号功功率确定导向矢量可能的误差范围，实现算法参数的自适应选取；
3．在多种导向矢量失配的情况下进行仿真，验证本文算法性能。
仿真结果表明，本文算法明显提高了波束形成器的性能，且优于其它算法。

关键词：自适应波束形成导向矢量失配鲁棒性最差性能最优
Abstract
Steering vector mismatch is inevitable in practical adaptive beamforming system,
which leads to poor performance of Capon beamformer. Therefore, it is of great
significance to research on the robustness of adaptive beamforming algorithms.
Although algorithms based on uncertainty set have robustness against steering vector
mismatch, they do not perform well enough in the presence of a large mismatch error. In
this thesis, an iterative worst-case performance optimization algorithm based on error
estimation is proposed, which concludes three keys as follows:
1. It improves the output signal to interference-plus-noise ratio by iteratively
utilizing worst-case performance optimization under a small uncertainty set;
2. Set different stop conditions under different signal-to-noise ratio conditions, and
select parameter of the algorithm adaptively by maximizing the output signal power to
determine the probable range of the actual steering vector;
3. The performance of the proposed algorithm is validated by computer simulations
with several types of steering vector mismatch.
Simulation results show that the proposed method obviously improves the
performance of the beamformer compared with other existing methods.

Keywords: Adaptive Beamforming Steering Vector Mismatch Robustness
Worst-Case Performance Optimization
目录
第一章绪论绪论绪论 ............................