Parallel Net: Frequency-Decoupled Neural Network for DOA Estimation in Underwater Acoustic Detection

Under wideband interference conditions, traditional neural networks often suffer from low accuracy in single-frequency direction-of-arrival (DOA) estimation and face challenges in detecting single-frequency sound sources. To address this limitation, we propose a novel model called Parallel Net. The architecture adopts a frequency-parallel design: it first employs a recurrent neural network, the generalized feedback gated recurrent unit (GFGRU), to independently extract features from each frequency component, and then it fuses these features through an attention mechanism. This design significantly enhances the network’s capability in estimating the DOA of single-frequency signals. The simulation results demonstrate that when the signal-to-noise ratio (SNR) exceeds −10 dB, Parallel Net achieves a mean absolute error (MAE) below 2°, outperforming traditional frequency-coherent neural networks and the MUSIC algorithm, and reduces the error to half that of classical beamforming (CBF). Further validation on the SWellEx-96 experiment confirms the model’s effectiveness in detecting single-frequency sources under wideband interference. Parallel Net exhibits superior sidelobe suppression and fewer spurious peaks compared to CBF, achieves higher accuracy than MUSIC, and produces smoother and more continuous DOA trajectories than conventional neural network models.