In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA). Based on an initial incomplete ULA measurement, our strategy is to choose a sparse subset of array elements for measuring the next snapshot. Then, we use a Hankel-structured matrix completion to interpolate for the missing ULA measurements. Finally, the source DOAs are estimated using a subspace method such as Prony on the fully recovered ULA. We theoretically provide a sufficient bound for the number of required samples (array elements) for perfect recovery. The numerical comparisons of the proposed method with existing techniques such as atomic-norm minimization and off-the-grid approaches confirm the superiority of the proposed method.
翻译:在本文中,我们研究使用一个稀有抽样的统一线性阵列估计抵达方向的问题。根据最初不完整的ULA测量方法,我们的战略是选择一组稀少的阵列元素来测量下一个快照。然后,我们用一个Hankel结构化的矩阵完成为缺失的ULA测量进行内插。最后,源的DOA使用一种子空间方法,如完全回收的ULA上的Prony,估计来源的DOA使用一种亚空间方法。我们理论上为完全回收所需的样品数量(阵列元素)提供了足够界限。拟议方法与现有技术,如原子-规范最小化和离网方法的数字比较证实了拟议方法的优越性。