As a generalized coprime array structure, the coprime array with displaced subarrays (CADiS) allows a large minimum inter-element spacing by introducing a specific displacement between two sparse subarrays. While this structure can effectively reduce mutual coupling, the holes in its difference co-array greatly decrease the achievable number of uniform degrees of freedom (DOFs). In this paper, we first provide a complete characterization for the hole locations in the difference co-array generated by a tailored CADiS (tCADiS) as the union of four subsets of locations related via simple symmetry properties. We then introduce two representation approaches for the hole locations, revealing that the latter can be generated from the differences between sensor locations in the subarray of tCADiS and a small uniform linear array, referred to as a padded subarray. Subsequently, we propose novel padded coprime arrays (PCAs) by incorporating the padded subarray into tCADiS to enlarge the consecutive segments in the difference co-array. This not only contributes to increase the number of available uniform DOFs, but also helps mitigating the mutual coupling by limiting the number of sensor pairs with small separations. Finally, numerical simulation results are provided to demonstrate the superiority of PCAs over existing sparse array configurations in terms of DOF, mutual coupling and DOA estimation accuracy.

中文翻译：

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用于改进 DOA 估计的填充互质阵列：利用孔表示和填充策略

作为广义的互质阵列结构，具有位移子阵列的互质阵列（CADiS）通过在两个稀疏子阵列之间引入特定位移来允许大的最小元素间距。虽然这种结构可以有效减少相互耦合，但其差异共阵列中的孔大大降低了可实现的均匀自由度（DOF）数量。在本文中，我们首先对由定制的 CADiS (tCADiS) 生成的差异共阵列中的孔位置提供完整的表征，作为通过简单对称属性相关的四个位置子集的联合。然后，我们介绍了孔位置的两种表示方法，表明后者可以从 tCADiS 子阵列中的传感器位置与小的均匀线性阵列之间的差异中生成，称为填充子数组。随后，我们通过将填充子阵列合并到 tCADiS 中来扩大差异共阵列中的连续段，从而提出了新的填充互质阵列（PCA）。这不仅有助于增加可用的均匀自由度的数量，而且还有助于通过限制具有小间距的传感器对的数量来减轻相互耦合。最后，提供了数值模拟结果，以证明 PCA 在 DOF、互耦合和 DOA 估计精度方面优于现有稀疏阵列配置。但也有助于通过限制小间距传感器对的数量来减轻相互耦合。最后，提供了数值模拟结果，以证明 PCA 在 DOF、互耦合和 DOA 估计精度方面优于现有稀疏阵列配置。但也有助于通过限制小间距传感器对的数量来减轻相互耦合。最后，提供了数值模拟结果，以证明 PCA 在 DOF、互耦合和 DOA 估计精度方面优于现有稀疏阵列配置。