Improving the Evaluation of Weak Underwater Radiated Noise Levels Using Probabilistic Geometric Spectral Subtraction

Underwater radiated noise emitted by ships threatens marine ecosystems and impacts naval operations reliant on precise acoustic signatures. Measuring weak underwater radiated noise levels (URNLs) is challenged by pervasive background noise and interference, insufficient signal-plus-noise-to-noise ratio (SNNR) of a single hydrophone system, and degraded performance in near-field low-emission scenarios. In this paper, we adapt the probabilistic geometric spectral subtraction approach (PGA) previously established in speech signal processing to enhance the weak underwater radiated noise in a single hydrophone measurement. A context-aware confidence parameter for background noise estimation is embedded within the gain function, addressing the non-stationary characteristics of ship noise and dynamic underwater interference. Validated via numerical simulations and sea trials, the proposed method enhances the SNNR of the received hydrophone signal, balancing the background noise suppression and source signal preservation critical to ship radiated noise level evaluation. It improves the radiated noise level estimation reliability, effectively mitigating near-field and low-SNNR challenges, and provides a robust technical solution for weak noise level assessment, facilitating the advancement of marine acoustic measurement with engineering applicability.