In this paper, we address the joint estimation problem of elevation, azimuth, and polarization with nested array consists of complete six-component electromagnetic vector-sensors (EMVS). Taking advantage of the tensor permutation, we convert the sample covariance matrix of the receive data into a tensorial form which provides enhanced degree-of-freedom. Moreover, the parameter estimation issue with the proposed model boils down to a Vandermonde constraint Canonical Polyadic Decomposition problem. The structured least squares estimation of signal parameters via rotational invariance techniques is tailored for joint auto-pairing elevation, azimuth, and polarization estimation, ending up with a computational efficient method that avoids exhaustive searching over spatial and polarization region. Furthermore, the sufficient uniqueness analysis of our proposed approach is addressed, and the stochastic Cramér-Rao bound for underdetermined parameter estimation is derived. Simulation results are given to verify the effectiveness of the proposed method.

在本文中，我们解决了由完整的六分量电磁矢量传感器（EMVS）组成的嵌套阵列的仰角，方位角和极化的联合估计问题。利用张量置换，我们将接收数据的样本协方差矩阵转换为张量形式，从而提供增强的自由度。此外，所提出模型的参数估计问题归结为范德蒙约束正则多态分解问题。通过旋转不变性技术对信号参数进行结构化的最小二乘估计是针对联合自动配对仰角，方位角和极化估计量身定制的，最终以一种计算有效的方法避免了在空间和极化区域上进行详尽搜索。此外，解决了我们提出的方法的充分唯一性分析，并推导了不确定参数估计的随机Cramér-Rao界。仿真结果证明了该方法的有效性。