一种基于次元分析技术的鲁棒波束形成算法

A Robust Beamforming Algorithm Based on Minor Component Analysis Technique

针对实际应用中先验知识存在偏差的问题,基于权向量长度恒定的常规线性约束波束形成算法,提出一种权向量长度恒定的最差情况性能优化波束形成算法.分析了神经次元分析(MCA)学习规则与该波束形成优化问题在数学描述上的相似性,利用神经MCA学习规则实现鲁棒自适应波束形成.仿真结果表明,与基于线性约束的波束形成算法相比,该算法具有更强的信号跟踪能力和干扰抑制能力,并且对信号方向向量的偏差具有更强的鲁棒性.

Considering that prior knowledge has errors in practical application, a worst-case performance optimization beamforming algorithm with invariable weight vector length is presented instead of the conventional one that is linearly constrained. Analyzes the mathematical similarity between the neural minor component analysis （MCA） learning rule and beamforming optimization problem. Then, the neural MCA learning rule is used to implement robust adaptive bearnforming. Computer simulations show that the proposed algorithm has stronger signal traekability and higher resistance to interference, and it is robuster in the presence of signal steering vector errors in cornparison with the linearly constrained beamforming algorithm.