A Deep Multiscale Wavelet Scattering-Attention Model for Real-Time Underwater DoA Estimation

Direction-of-arrival (DoA) estimation is vital for underwater target localisation but is challenged by multipath propagation and noise in dynamic ocean environments. To tackle such issues, we introduce the multiscale attentive wavelet scattering fusion (MAWSF) for DoA estimation in the challenging underwater environment. This MAWSF is a deep learning-based network, trained on simulated underwater data, capable of generalising to real oceanic scenarios. MAWSF performs multi-scale feature extraction using directional convolutions and wavelet scattering transforms to capture spatial patterns and enhance robustness to noise. It also employs attention mechanisms to model global dependencies and enhance context awareness. To emulate real oceanic conditions, we simulate multipath propagation along with varying source-receiver depths, ranges, and ambient noise levels. These simulations are used to generate multi-channel spatial covariance matrices labelled with a one-hot encoded true DoAs vector for training. MAWSF is evaluated on the real SWellEx-96 dataset, where it demonstrated high angular resolution in offgrid DoA estimation. It achieves an RMSE of$14.76^{\circ }$, surpassing the performance of state-of-the-art networks. Unlike traditional methods, it requires no prior knowledge of noise or multipath counts, making it well-suited for real-time applications.