To address the challenge of degraded DOA estimation performance under array errors and low signal-to-noise ratio conditions, this paper proposes an Enhanced Spatio-Temporal Network (E-STNet). This network adopts a dual-source input architecture. By integrating multi-scale pooling and a hybrid Long Short-Term Memory-Transformer (LSTM-Transformer) encoder, the network jointly refines spatial feature representations and captures multi-granularity temporal dependencies. Simulation results demonstrate that, under challenging scenarios such as array errors, low Signal-to-Noise Ratio (SNR), and closely spaced sources, E-STNet achieves higher estimation accuracy and stronger robustness than conventional algorithms and existing deep learning methods, providing an effective solution for DOA estimation in complex environments.
Zhao et al. (Wed,) studied this question.