一种基于导向矢量约束的恒模盲波束形成算法  

A constant modulus blind beamforming algorithm based on constrained direction vectors

作　　者：刘可[1] 钱华明[1] 马俊达[1] 

机构地区：[1]哈尔滨工程大学自动化学院,哈尔滨150001

出　　处：《哈尔滨工业大学学报》2016年第9期151-156,共6页Journal of Harbin Institute of Technology

基　　金：国家自然科学基金(61573113);哈尔滨市科技创新人才研究专项资金(优秀学科带头人)资助项目(2014RFXXJ074)

摘　　要：针对阵列信号波达角(direction of arrival,DOA)先验信息已知的情况,利用信号的恒模特性,在卡尔曼滤波(Kalman filter,KF)结构下,提出一种附加阵列导向矢量约束的自适应波束形成算法.对约束情况下的卡尔曼滤波目标函数运用拉格朗日乘子法,求得约束条件下的最优估计表达式,并将其推广到无迹卡尔曼滤波(unscented Kalman filter,UKF)算法中,通过约束迭代算法对阵列估计信号的导向角施加约束,实现约束UKF自适应波束形成算法的最优权值分配.仿真过程中,用所提算法与约束恒模迭代最小二乘算法和约束最小方差迭代最小二乘算法作对比,表明表明,该算法在收敛速度、信噪比、稳健性、跟踪性能方面具有较好的性能.Aiming at the case of the known knowledge of the constant modulus feature, a new blind adaptive beamforming direction of arrival of the desired signal, by using the algorithm is proposed under the frame of the Kalman filter. According to the Lagrange multiplier method, the optimal estimation expression of the system states can be derived by minimizing the constrained cost function. Then, the optimal weight vector of the adaptive beamformer can be obtained by using the iteration and update equations of the unscented Kalman filter. In the simulation, the proposed algorithm is compared with the constrained constant modulus recursive least square （CCM-RLS） and constrained minimum variance recursive least square （CMV-RLS） to demonstrate its effectiveness in the terms of the convergence speed, signal to interference plus noise ratio, robustness to changeable environments and tracking capability in the non-stationary condition.

关 键 词：信号处理 自适应滤波 无迹卡尔曼滤波 波束形成 约束优化技术 

分 类 号：TN911[电子电信—通信与信息系统]