基于特征向量的阵列误差矩阵最优闭式解

阵列互耦和幅相误差的综合作用会严重影响MUSIC算法的测向性能. 该文重点研究了由互耦和幅相误差 引起的阵列误差校正问题，给出3 种阵列误差矩阵校正算法. 它们具有相同的计算模式和理论框架，均可通过计算某 个Hermite矩阵最小特征值对应的特征向量获得最优闭式解. 算法I未利用阵列误差矩阵的任何性质，算法II利用了阵列 误差矩阵的稀疏性，算法III利用了某些规则阵列的阵列误差矩阵的特殊结构. 仿真实验比较了3 种校正算法的估计精 度，结果表明，尽可能利用阵列误差矩阵的特殊性质有利于提高阵列误差矩阵的校正精度.

Optimal Closed-Form Solution to Array Error Matrix Based on Eigenvector

The combined effects of mutual coupling and amplitude-phase errors have negative impact on the direction-finding performance of the MUSIC algorithm. In this work, we calibrate array errors induced by the mutual coupling effect and amplitude-phase errors. Three algorithms are presented, which have the same computational mode and theoretical framework, and provide optimal closed-form solutions to the array error matrix based on the eigenvector of the Hermite matrix corresponding to the smallest eigenvalue. The first algorithm does not make use of any property of the array error matrix, the second uses sparseness of the array error matrix, and the third makes fully use of the special structure of the array error matrix for some regular arrays. Performances of parameter estimation of the three algorithms are compared by simulation. The results show that the calibration precision of array error matrix can be improved if the algorithm uses more special properties of the array error matrix.