Two-Dimensional Direction-of-Arrival and Polarization Parameter Estimation Using Parallel Co-Prime Polarization Sensitive Array

Target detection is critical in many mission critical sensors and sensor network (MC-SSN) applications. For target detection in complicated electromagnetic environment, DOA estimation using polarization sensitive array (PSA) has been receiving increased attentions. In this paper, we propose the parallel co-prime polarization sensitive array (PCP-PSA) which consists of the cocentered orthogonal dipole triads (CODTs) to estimate two-dimensional direction-of-arrival (2D DOA) and polarization parameters. The degrees of freedom (DOFs) have been extended due to the co-prime structure, so that the more signals can be detected and the estimation accuracy is improved. In order to reduce the computation complexity, we construct a new cross-covariance matrix based on the CODTs, which converts the two-dimensional DOA estimation into two independent one-dimensional DOA estimations. Then, the spatial smoothing-based multiple signal classification algorithm(MUSIC) and the sparse representation-based method are applied to estimate 2D DOA with only one-dimensional (1D) peak searching and 1D dictionary, respectively. Finally, the polarization parameters are estimated by using the cross-covariance matrix between components of electric field vector. Compared with previous PSA-based algorithms, the proposed algorithm based on PCP-PSA can solve the underdetermined 2D DOA and polarization parameters estimation problem and has better estimation accuracy. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity and estimation accuracy.

目标检测在许多关键任务传感器和传感器网络（MC - SSN）应用中至关重要。对于复杂电磁环境中的目标检测，利用极化敏感阵列（PSA）进行波达方向（DOA）估计受到了越来越多的关注。在本文中，我们提出了平行互质极化敏感阵列（PCP - PSA），它由共心正交偶极子三元组（CODTs）组成，用于估计二维波达方向（2D DOA）和极化参数。由于互质结构，自由度（DOFs）得到了扩展，从而可以检测更多的信号，并且提高了估计精度。为了降低计算复杂度，我们基于CODTs构建了一个新的互协方差矩阵，它将二维DOA估计转化为两个独立的一维DOA估计。然后，分别应用基于空间平滑的多信号分类算法（MUSIC）和基于稀疏表示的方法，仅通过一维（1D）峰值搜索和一维字典来估计2D DOA。最后，利用电场矢量分量之间的互协方差矩阵来估计极化参数。与先前基于PSA的算法相比，基于PCP - PSA的所提算法能够解决欠定的二维DOA和极化参数估计问题，并且具有更好的估计精度。理论分析和仿真结果在计算复杂度和估计精度方面验证了所提方法的有效性。