This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays that are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation

翻译：本文研究了利用不同稀疏子阵列进行非相干波达方向（DOA）估计的问题。具体而言，我们提出了一种基于低秩与稀疏恢复优化问题的多测量向量（MMV）模型用于非相干DOA估计。此外，我们开发了两种不同的实用策略来获取稀疏阵列与子阵列：i) 从主稀疏阵列几何结构生成子阵列（I型稀疏阵列）；ii) 直接设计稀疏子阵列并将其组合成整体稀疏阵列（II型稀疏阵列）。数值结果表明，所提出的MMV模型能有效利用多组数据记录，且II型非相干稀疏阵列在DOA估计性能上表现更优。