Stochastic Subspace Identification with Kalman filter for ambient sensor noise reduction in wind tunnel test

This study introduces the integration of Stochastic Subspace Identification (SSID) with the Kalman filter as a general-purpose method for mitigating ambient sensor noise in wind tunnel tests. Filter parameters are estimated using SSID, a robust data-driven technique for extracting system dynamics under unknown input conditions. The proposed SSID-Kalman filter is validated through numerical simulations and experimental data from three aeroelastic tests, including acceleration and strain measurements from free-standing bridge towers, as well as voltage signals from a PVDF piezoelectric film excited by the aeroelastic response of an airfoil-based wind energy harvester. Numerical validation confirms the filter's capability to recover ground-truth signals by suppressing the noise. In wind tunnel experiments, the filter reduces noise across both low- and high-frequency ranges while preserving resonant components of the structural response. The signal-to-noise ratio gains reach 98.4 % for acceleration, 79.4 % for strain, and over 600 % for voltage. Compared with spectral subtraction, a method recently proposed by the first author, the SSID-Kalman filter demonstrates superior performance, particularly when dominant vibrational modes have only marginal energy above the noise floor. The method provides wind tunnel practitioners with a flexible and robust tool for sensor denoising and is broadly applicable to dynamic measurements representable within a state-space framework. • This study integrates Stochastic Subspace Identification (SSID) with the Kalman filter as a general-purpose method for reducing ambient sensor noise. • The method was validated using acceleration and strain measurements from bridge towers and extended to voltage signals from a PVDF piezoelectric film.The method was validated using acceleration and strain signals from bridge towers, voltage signals from a piezoelectric film. • The SSID-Kalman filter effectively suppresses noise, reduces signal standard deviation and improves signal-to-noise ratio. • The filter outperforms spectral subtraction when dominant vibrational mode energy is only slightly higher than ambient noise.