Abstract The conventional coprime array consists of two uniform linear subarrays to construct an effective difference coarray with desirable characteristics. Such linear coprime arrays only provide one-dimensional (1-D) direction-of-arrival (DOA) estimation. In this paper, we propose a novel coprime array configuration with parallel subarrays, along with an effective method for two-dimensional (2-D) DOA estimation. The 2-D DOA estimation problem is cast as two separate 1-D problems for reduced complexity and is solved using one of the two mechanisms based on the number of sensors and that of sources. When there are less sources than the number of sensors, subspace-based and rank-reduction estimation (RARE) techniques are sequentially applied to the physical array output. On the other hand, when the number of sources is equal to or larger than that of sensors, a virtual difference coarray is formed and group sparse reconstruction and least squares operations are then applied. In both scenarios, the proposed methods automatically pair the corresponding azimuth and elevation angles. The proposed methods resolve up to MN sources using 2 M + N − 1 sensors, which are the same as in the 1-D DOA estimation using conventional coprime arrays. Simulations results are presented delineating both the accuracy and resolution capability of the proposed method.

中文翻译：

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使用并行互素阵列改进二维 DOA 估计

摘要 传统的互质阵列由两个均匀的线性子阵列组成，以构建具有理想特性的有效差分共阵列。这种线性互素阵列仅提供一维 (1-D) 到达方向 (DOA) 估计。在本文中，我们提出了一种具有并行子阵列的新型互质阵列配置，以及一种用于二维 (2-D) DOA 估计的有效方法。二维 DOA 估计问题被转换为两个单独的一维问题以降低复杂性，并使用基于传感器数量和源数量的两种机制之一来解决。当源数量少于传感器数量时，基于子空间的降阶估计 (RARE) 技术将依次应用于物理阵列输出。另一方面，当源的数量等于或大于传感器的数量时，形成虚拟差分共阵列，然后应用组稀疏重建和最小二乘运算。在这两种情况下，所提出的方法都会自动配对相应的方位角和仰角。所提出的方法使用 2 M + N − 1 个传感器解析多达 MN 源，这与使用传统互素阵列的 1-D DOA 估计相同。仿真结果描述了所提出方法的精度和分辨率能力。所提出的方法使用 2 M + N − 1 个传感器解析多达 MN 源，这与使用传统互素阵列的 1-D DOA 估计相同。仿真结果描述了所提出方法的精度和分辨率能力。所提出的方法使用 2 M + N − 1 个传感器解析多达 MN 源，这与使用传统互素阵列的 1-D DOA 估计相同。仿真结果描述了所提出方法的精度和分辨率能力。