Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a virtual large uniform linear array (ULA), which is then processed by subspace DoA estimators. However, these method assume that the signals are non-coherent and the array is calibrated; the latter often challenging to achieve in sparse arrays, where one cannot access the virtual array elements. In this work, we propose Sparse-SubspaceNet, which leverages deep learning to enable subspace-based DoA recovery from sparse miscallibrated arrays with coherent sources. Sparse- SubspaceNet utilizes a dedicated deep network to learn from data how to compute a surrogate virtual array covariance that is divisible into distinguishable subspaces. By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.

翻译：稀疏阵列通过非均匀排列，能够在不增加天线单元数量的情况下解析更多的波达方向（DoA）。传统方法通常通过重构虚拟均匀线性阵列（ULA）的协方差来实现这一目标，进而利用子空间DoA估计器进行处理。然而，这些方法假设信号是非相干的且阵列已校准——后者在稀疏阵列中往往难以实现，因为无法直接访问虚拟阵列单元。本文提出Sparse-SubspaceNet，该方法利用深度学习从校准不良的稀疏阵列中恢复相干源的子空间DoA。Sparse-SubspaceNet通过专用深度网络从数据中学习如何计算可分解为可辨识子空间的替代虚拟阵列协方差。通过这种方式，我们能够处理相干源和校准不良的稀疏阵列，同时保持基于模型的子空间DoA估计器的可解释性与适用性。