论文标题
使用巨大/超质量接收阵列的两个无人机发射器的快速功率迭代DOA估计器
Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array
论文作者
论文摘要
为了在未来的无线网络中提供无人驾驶飞机（UAV）发射极的快速方向查找（DF），构建了大量多重输入多重输出（MIMO）接收器阵列的低复杂到达方向（DOA）估计结构。在本文中，我们提出了两种策略，以解决接收信号协方差矩阵的特征值分解引起的极高复杂性。首先，提出了一种快速的功率旋转不变性（RPI-RI）方法，该方法采用了通过电源迭代产生的信号子空间，以通过子阵列之间的旋转不变性来获得最终方向估计。 RPI-RI以大量性能损失的成本降低了重大的复杂性。为了进一步降低复杂性并提供良好的方向测量结果，提出了快速的功率词语多项式生根（RPI-PR）方法，该方法利用噪声子空间与多项式求解方法相结合，以获得最佳的方向估计。此外，分析了初始矢量选择对电源迭代中收敛性的影响，尤其是当初始矢量与入射波正交时。仿真结果表明，在计算复杂性方面，这两种提出的方法的表现优于常规DOA估计方法。特别是，RPIPR方法的复杂性比常规方法低两个以上的数量级，并且在CRLB附近的性能。此外，可以验证的是，初始向量和相对误差对计算复杂性的性能有重大影响。
To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity.
