Predictive modeling of wavefront error using dynamic mode decomposition

Benjamin D. Shaffer; Austin J. McDaniel; Christopher C. Wilcox

doi:10.1117/12.2569869

21 August 2020 Predictive modeling of wavefront error using dynamic mode decomposition

Benjamin D. Shaffer, Austin J. McDaniel, Christopher C. Wilcox

Proceedings Volume 11490, Interferometry XX; 114900E (2020) https://doi.org/10.1117/12.2569869
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

In an airborne directed energy system, air density variations around an aircraft distort a beam’s wavefront, resulting in degraded performance after propagation. Adaptive Optics (AO) can be used to correct for these aero-optical aberrations in the wavefront to give increased performance. However, in some conditions state-of-the-art AO systems have insufficient spatial and temporal bandwidth to accurately maintain high system performance. Predictive AO control is a promising method to mitigate these problems and therefore improve beam quality. This manuscript builds on previous research with novel Digital Holographic (DH) Wavefront Sensor (WFS) data measuring aero-optical disturbances from the wind tunnel at the Air Force Research Laboratory (AFRL) Aero-Effects Laboratory (AEL) to present a Dynamic Mode Decomposition (DMD) based predictive modeling tool. DMD is a light-weight, equationfree, dimensionality reduction algorithm that can be used to isolate spatio-temporal patterns in a data set into physically meaningful modes. These can be used to predict future states from a given wavefront. This manuscript demonstrates application of this method to data acquired through AFRL as well as discussing the underlying mechanisms.

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Benjamin D. Shaffer, Austin J. McDaniel, and Christopher C. Wilcox "Predictive modeling of wavefront error using dynamic mode decomposition", Proc. SPIE 11490, Interferometry XX, 114900E (21 August 2020); https://doi.org/10.1117/12.2569869

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