For a passive direction of arrival (DOA) measurement system using massive multiple input multiple output (MIMO), the complexity of the covariance matrix decompositionbased DOA measurement method is extremely high. To significantly reduce the computational complexity, two strategies are proposed. Firstly, a rapid power-iterative estimation of signal parameters via rotational invariance technique (RPI-ESPRIT) method is proposed, which not only reduces the complexity but also achieves good directional measurement results. However, the general complexity is still high. In order to further the complexity, a rapid power-iterative root Multiple Signal Classification (RPIRoot-MUSIC) method is proposed. Simulation results show that the two proposed methods outperform the classical DOA estimation method in terms of computational complexity. In particular, the lowest complexity achieved by the RPI-Root-MUSIC method is about two-order-magnitude lower than that of Root-MUSIC in terms of FLOP. In addition, it is verified that the initial vector and relative error have a substantial effect on the performance of computational complexity.

翻译：对于使用大量多重输入多重产出的被动抵达方向测量系统(DOA),共振矩阵分解数据数据数据测量方法的复杂程度极高,为大幅降低计算复杂性,提出了两个战略:首先,建议通过旋转变换技术(RPI-ESPRIT)对信号参数进行快速电离估计,这不仅降低了复杂性,而且取得了良好的定向测量结果;然而,一般复杂性仍然很高;为了进一步推进复杂性,建议采用快速电源-电极根多信号分类(RPIRooot-MUSCIC)方法;模拟结果显示,两种拟议方法在计算复杂性方面超过了传统的DOA估计方法;特别是,RPI-Rooot-MUSIC方法实现的最低复杂性在FLOP方面比根-MUSICT低2级-磁度。此外,还核实初始矢量和相对误差对计算复杂性的绩效产生了实质性影响。