Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, where multiple snapshots aren't accessible, and the importance of minimizing hardware costs. Low-rank Hankel matrix completion has been proposed to interpolate the missing elements in SLAs. However, the solvers of matrix completion, such as iterative hard thresholding (IHT), heavily rely on expert knowledge of hyperparameter tuning and lack task-specificity. Besides, IHT involves truncated-singular value decomposition (t-SVD), which has high computational cost in each iteration. In this paper, we propose an IHT-inspired neural network for single-snapshot DOA estimation with SLAs, termed IHT-Net. We utilize a recurrent neural network structure to parameterize the IHT algorithm. Additionally, we integrate shallow-layer autoencoders to replace t-SVD, reducing computational overhead while generating a novel optimizer through supervised learning. IHT-Net maintains strong interpretability as its network layer operations align with the iterations of the IHT algorithm. The learned optimizer exhibits fast convergence and higher accuracy in the full array signal reconstruction followed by single-snapshot DOA estimation. Numerical results validate the effectiveness of the proposed method.

翻译：单快照到达角估计在汽车MIMO雷达领域引起广泛关注，这源于汽车场景的动态特性——无法获取多个快照，以及降低硬件成本的重要性。低秩Hankel矩阵补全方法已被提出用于插值稀疏线性阵列中的缺失元素。然而，迭代硬阈值等矩阵补全求解器严重依赖超参数调优的专家知识且缺乏任务特异性。此外，IHT算法涉及截断奇异值分解，每次迭代计算成本高昂。本文提出一种IHT启发的神经网络用于稀疏线性阵列单快照DOA估计，命名为IHT-Net。我们采用循环神经网络结构参数化IHT算法，并集成浅层自编码器替代t-SVD，在降低计算开销的同时通过监督学习生成新型优化器。IHT-Net保持强可解释性，其网络层操作与IHT算法迭代过程严格对应。该学习型优化器在完整阵列信号重建及后续单快照DOA估计中展现出快速收敛与更高精度。数值结果验证了所提方法的有效性。