Swin Transformer for Robust DOA Estimation of Coherent Sources Using Uniform Linear Arrays

ABSTRACT

In this paper, a robust direction‐of‐arrival (DOA) estimation method based on the swin transformer (DOA‐SwinT) is proposed for estimating the DOAs of narrowband coherent sources using a uniform linear array under challenging conditions, such as low signal‐to‐noise ratio (SNR) and a limited number of snapshots. The swin transformer (SwinT) achieves high efficiency and accuracy in classification tasks owing to its low computational complexity and high performance, which is enabled by its window attention mechanism and hierarchical architecture. The proposed DOA‐SwinT fuses both low‐level and high‐level features extracted from array signal data, thereby improving DOA estimation accuracy. Furthermore, DOA‐SwinT can handle coherent signals and exhibit good DOA estimation performance. Simulation results demonstrate that the proposed method achieves substantially higher estimation accuracy than existing deep‐learning‐based methods and traditional high‐resolution methods for coherent signals under equivalent SNR and snapshot conditions.