基于改进PSO的对角加载波束形成算法

Diagonal Loading Beamforming Algorithm Based on Improved PSO

当协方差矩阵和导向矢量估计存在误差时,Capon波束形成算法性能急剧下降。对角加载能够提升Capon波束形成算法对误差的鲁棒性,但是最优加载因子的确定是当前的难题。提出一种基于改进粒子群优化(IPSO)算法的对角加载波束形成算法,首先将加载因子与协方差矩阵特征值谱联系起来,利用协方差矩阵特征值谱的分布特性确定最优加载因子的取值范围,然后利用PSO算法进行全局寻优,针对PSO固定搜索步长的缺点,提出一种自适应变步长迭代方法对其优化,加速PSO算法的收敛速度。基于仿真数据的结果表明,所提方法可以获得较好的波束形成性能。

When the covariance matrix and steering vector estimation have errors,the performance of Capon beamforming algorithm drops sharply.Diagonal loading can improve the robustness of Capon beamforming algorithom to errors,but the determination of the optimal loading factor is a current problem.A diagonal loading beamforming algorithm based on improved particle swarm optimization(IPSO)is proposed.First,the loading factor is connected with the covariance matrix eigenvalue spectrum,and the distribution characteristics of the covariance matrix eigenvalue spectrum are used to determine the value range of the optimal loading factor.Then PSO algorithm is used to perform global optimization of the optimal loading factor.Aiming at the shortcoming of PSO fixed step size in search,an adaptive variable step-size iteration method is proposed to optimize the PSO algorithm,which accelerates the convergence speed of PSO algorithm.The results based on simulation data show that using the proposed method can achieve better beamforming performance.