Deep Learning-Based Source Localization with Interference Striation of a Towed Horizontal Line Array

The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of Experts Localization Network) for deep-sea sound source localization, leveraging interference structures in range-frequency domain signals from a towed horizontal line array. Unlike traditional correlation-based methods constrained by time-varying ocean environments and low signal-to-noise ratios, the model employs multi-expert and multi-task learning to extract interference periods from single-frame data, enabling robust estimation of source range and depth. Simulation results demonstrate its superior performance in the deep-sea shadow zone, achieving a range localization error of 0.029 km and a depth error of 0.072 m. The method exhibits strong noise robustness and delivers satisfactory results across diverse deep-sea zones, with optimal performance in shadow zones and secondary effectiveness in the direct arrival zone.