基于集员共轭梯度的约束自适应波束成形算法

Constrained adaptive beamforming algorithm based on set-membership and conjugate gradient

针对传统波束成形计算复杂度过大的问题,提出一种基于集员共轭梯度的约束自适应波束成形算法。运用共轭梯度算法原理,在期望信号功率保留的约束条件下使输出方差最小,得到权重向量,避免计算输入信号的协方差逆矩阵,有效达到收敛。集员方法运用时变边界约束条件,实行数据选择性更新,减少计算复杂度。该算法运用集员方法和共轭梯度,避免重复计算,得到有效的权重向量,保证良好的收敛性能。又对算法进行计算复杂度和收敛性能分析。仿真结果表明,与其他传统算法相比,该算法在保证良好的收敛性能的同时,大大减少了计算复杂度。

To solve the problem of excessive computational complexity of traditional beamforming,a constrained adaptive beamforming algorithm based on set-membership and conjugate gradient is proposed.By using the principle of the conjugate gradient algorithm,the output variance is minimized under the constraint of keeping the desired signal power,then the weight vector is obtained,the calculation of the input signal covariance inverse matrix is avoided,and convergence is effectively achieved.Set-membership applies data-selective updates by using time-varying boundary constraints to reduce the computational complexity.The algorithm avoids repetitive computation and obtains effective weight vector by using the set-membership and conjugate gradient.Computational complexity and convergence performance analysis of the algorithm are provided.Simulation results show the enhanced convergence performance and low computational complexity of the proposed algorithm compared with traditional algorithms.