稀疏均匀非同心电磁矢量阵列的2D-DOA与极化联合估计

本文提出了一种稀疏均匀非同心电磁矢量传感器矩形阵列,针对该阵列提出了一种二维波达方向(2D-DOA)和极化参数的联合估计算法.首先利用稀疏均匀矩形阵列的旋转不变性得到周期性模糊的2D-DOA估计,然后提出一种简易的非同心电磁矢量传感器的2D-DOA估计算法来解模糊,再通过一些三角变换得到高精度无模糊的2D-DOA和极化参数估计,最后推导了该阵列多参量估计的闭式克拉美罗界.本文所提阵列的稀疏配置使得在不增加阵元数和硬件复杂度情况下有效扩展了阵列物理孔径,且由于矢量传感器的使用获得了极化分集,使得2D-DOA的估计精度大大增加.此外本文方法能得到2D-DOA和极化参数之间的自动配对,更为重要的是该阵列使用非同心电磁矢量传感器构成,解决了同心电磁矢量传感器互耦严重、硬件设计困难的问题.仿真结果证明了本文多参量估计算法的有效性.

Joint 2D-DOA and polarization estimation using a sparse uniform array of spatially non-collocating electromagnetic vector sensors

In this paper, a sparse uniform rectangular array of spatially non-collocating electromagnetic vector sensors is proposed. And a two-dimensional direction of arrival （2D-DOA） and polarization estimation algorithm for the array is proposed. First, the rotational invariance of the sparse uniform rectangular array is utilized to yield cyclically aliasing 2D-DOA estimation. Then we propose a simple non-aliasing 2D-DOA estimation algorithm with the spatially non-collocating electromagnetic vector sensor to solve the cyclically aliasing. And then the nonaliasing high accuracy 2D-DOA and polarization estimation is obtained by some triangular operations. Finally, the closed-form Cramer-Rao bound of the multiple parameters estimation is derived. The sparse configuration of the proposed array is capable of extending array aperture without increasing the number of sensors and the hardware complexity. The proposed array can also obtain the polarization diversity offered by electromagnetic vector sensor. The two factors can improve the 2D-DOA estimation accuracy greatly. In addition, the proposed algorithm provides the automatic pairing between the 2D-DOA and polarization parameters. And it is more important is that the proposed array consists of spatially non-collocating electromagnetic vector sensors, which can solve the problems of serious mutual coupling and difficult hardware design, yielded by spatially collocating electromagnetic vector sensor. Simulation results verify the effectiveness of the proposed multiple parameters estimation algorithm.