Nested U-Shape Network for Robust Direction-of-Arrival Estimation

This work proposes a new deep learning method by extracting multiple-scale features from both local and global aspects for Direction-of-Arrival (DOA) estimation tasks. A nested U-shaped convolutional neural network ($U^{2} Net$, where the superscript 2 refers to the nesting level) is exploited to predict the directions of two sources scattered in a 120-degree space. The highlights of $U^{2} Net$ are to design an inner U-net for local features and another outer U-net to fuse local features for global feature patterns. The DOA problem is designed as a multi-label classification task with on-grid patterns, and train data set is generated at low SNR regimes by specifying sample covariance matrix as input features and source direction as labels. Simulation results demonstrate that the $U^{2} Net$ achieves excellent and robust performances in multiple-target DOA estimation tasks, especially under challenging conditions with high noise levels, small snapshots and coherent sources. Moreover, the $U^{2} Net$ has a potential to resolve source angles within grid resolution. Finally, comparative studies verified that the $U^{2} Net$ outperforms traditional DOA methods as well as convolutional neural network with low SNR, limited snapshots and coherent sources presented.