一种用于波束形成技术的神经次元分析算法  

A Neural Minor Component Analysis Algorithm to Beamforming

作　　者：田丹[1] 张明[1] 

机构地区：[1]沈阳大学信息工程学院,辽宁沈阳110044

出　　处：《沈阳大学学报》2007年第2期56-59,共4页

摘　　要：基于自稳定次元分析学习规则,提出一种添加了惩罚项的次元分析(MCA)学习规则.分析了该学习规则的稳定性和收敛性,用于自适应波束形成技术的实现.采用带有约束条件的功率优化波束形成原理,利用白噪声敏感度控制和干扰的先验知识,提高了波束形成算法的鲁棒性.仿真实验表明,与用于实现鲁棒约束波束形成技术的FMCA学习规则相比,MCA学习规则具有更强的稳定性,能更有效地用于干扰抑制和实时信号跟踪.A minor component analysis （MCA） learning rule is presented which includes a penalty term on the self-stabilizing MCA learning rule. After a presentation of steady - state and convergence analysis, the learning rule is used for adaptive beamforming. Constrained beamformer power optimization principle is employed, which allows to improve the robustness of the beamforming algorithm by emphasizing white noise sensitivity control and prior knowledge about the disturbances. Computer simulations show the novel MCA learning rule has strong stability, interference resistance ability and real-time signal tracking ability, compared with the first minor component analysis （FMCA） learning rule which is used for robust constrained beamforming.

关 键 词：神经网络 次元铲析 自适应波束形成 鲁棒约束 

分 类 号：TN971.2[电子电信—信号与信息处理] TN911.7[电子电信—信息与通信工程]