Dynamic-Mode Decomposition for Aero-Optic Wavefront Characterization

Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the turbulent boundary layer of an airborne optical system. We leverage the forecasting capabilities of the dynamic mode decomposition (DMD) – an equation-free, data-driven method for identifying coherent flow structures and their associated spatiotemporal dynamics – in order to estimate future state wavefront phase aberrations to feed into an adaptive optic control loop. We specifically leverage the optimized DMD (opt-DMD) algorithm on a subset of the Airborne Aero-Optical Laboratory Transonic (AAOL-T) experimental dataset. Critically, opt-DMD allows for de-biasing of the forecasting algorithm in order to produce a robust, stable, and accurate future-state prediction, and the underlying DMD algorithm provides a highly interpretable spatiotemporal decomposition of the turbulent boundary layer and the resulting aberrations to the wavefront dynamics.