Sea-State-Aware Adaptive Filtering of Tidal Current Measurements under Wave- Induced Disturbances

Reliable tidal current measurements are vital for monitoring and assessing the performance of tidal turbine systems and other marine energy applications. In practice, these measurements are often affected by stochastic disturbances and wave-induced fluctuations that vary with sea-state conditions. Standard adaptive filtering techniques, such as LMS and RLS, are commonly used to clean these signals, but their performance can decline when disturbances change dynamically. In this study, we introduce a sea-state-aware adaptive filtering framework that incorporates environmental wave information into the filter adaptation process. By considering sea-state variations, the filters can adjust dynamically to changing noise conditions, improving the recovery and reliability of tidal signals. The framework is tested on tidal current datasets under stochastic and wave-induced disturbances. Results show improved signal fidelity, reduced estimation errors, and more stable operation compared to conventional LMS and RLS filters. This approach offers a practical solution for reliable tidal turbine monitoring and can be applied to other coastal and offshore measurement systems influenced by wave-induced noise.