ST-SSNet: Spatio-temporal feature fusion-based DOA estimation network for underwater array signals

For Underwater Internet of Things (UIoT) applications, the accurate and efficient estimation of the direction of arrival (DOA) is fundamental to technologies such as node localization and autonomous underwater vehicle (AUV) node cooperative communication. However, the low signal-to-noise ratio (SNR) and limited energy in underwater environments pose severe challenges to DOA estimation. Furthermore, existing methods typically require a large number of snapshots. To address these issues, this paper proposes the use of an adaptive wavelet denoising model to enhance the quality of underwater acoustic signals. Subsequently, a dual-branch space-time state space network (ST-SSNet) is proposed. This network consists of a time feature extraction branch (TFEB) and a space feature extraction branch (SFEB). The time branch incorporates gating units and time mixing functions into the state space model (SSM) within the Mamba framework to extract temporal features. The spatial branch uses one-dimensional convolutions in different directions and the convolutional block attention module (CBAM) to extract spatial features. Extensive simulation and sea trial experiments demonstrate that ST-SSNet outperforms other deep learning methods in various scenarios, while having lower computational complexity than other methods. Compared to ResNet18, the accuracy improves by 2.14%, and RMSE is reduced by 43.4%.