Quantifying time-varying wind-driven effects on matched-field localization: Mechanisms and a physics-coupled Bayesian approach

Matched-field processing is highly sensitive to environmental mismatch, yet most robust formulations emphasize static uncertainties more than time-evolving environmental forcing. This study examines a representative low-frequency shallow-water scenario in which wind-driven mixed-layer deepening reshapes the upper-ocean sound-speed profile and perturbs modal horizontal wavenumbers, producing accumulated phase errors, ambiguity-surface distortion, and systematic range bias. To organize these effects beyond a single operating point, a conditional modal phase-spread analysis is introduced to show how wind-driven degradation depends jointly on wind state, propagation range, frequency, and source depth relative to the mixed layer. A physics-coupled particle filter (PC-PF) is then proposed, in which wind speed is treated as a dynamic hidden state and estimated jointly with source range through an embedded reduced-order environmental model. Broadband numerical experiments are used to assess mechanism and tracking performances. For the representative storm-evolution scenario considered here, a conventional static-model broadband Bartlett processor develops kilometer-scale range errors, whereas the proposed PC-PF substantially reduces the root mean square error and preserves track continuity. The formulation is intended as a reduced-order, acoustically informed framework for dynamic environmental adaptation in time-varying conditions.