TY - JOUR
T1 - Sparse data-driven wavefront prediction for large-scale adaptive optics
AU - Cerqueira, Paulo
AU - Piscaer, Pieter
AU - Verhaegen, Michel
PY - 2021
Y1 - 2021
N2 - This paper presents a computationally efficient wavefront aberration prediction framework for data-driven control in large-scale adaptive optics systems. Our novel prediction algorithm splits prediction into two stages: A highresolution and a low-resolution stage. For the former, we exploit sparsity structures in the system matrices in a data-driven Kalman filtering algorithm and constrain the identified gain to be likewise sparse; for the latter, we identify a denseKalman gain and performcorrections to the suboptimal predictions of the former on a smaller grid. This novel prediction framework is able to retain the robustness to measurement noise of the standardKalman filter in a much more computationally efficient manner, in both its offline and online aspects, while minimally sacrificing performance; its data-driven nature further compensates for modeling errors. As an intermediate result, we present a sparsity-exploiting data-drivenKalman filtering algorithm able to quickly estimate an approximateKalman gain without solving the Riccati equation.
AB - This paper presents a computationally efficient wavefront aberration prediction framework for data-driven control in large-scale adaptive optics systems. Our novel prediction algorithm splits prediction into two stages: A highresolution and a low-resolution stage. For the former, we exploit sparsity structures in the system matrices in a data-driven Kalman filtering algorithm and constrain the identified gain to be likewise sparse; for the latter, we identify a denseKalman gain and performcorrections to the suboptimal predictions of the former on a smaller grid. This novel prediction framework is able to retain the robustness to measurement noise of the standardKalman filter in a much more computationally efficient manner, in both its offline and online aspects, while minimally sacrificing performance; its data-driven nature further compensates for modeling errors. As an intermediate result, we present a sparsity-exploiting data-drivenKalman filtering algorithm able to quickly estimate an approximateKalman gain without solving the Riccati equation.
UR - http://www.scopus.com/inward/record.url?scp=85109423146&partnerID=8YFLogxK
U2 - 10.1364/JOSAA.425668
DO - 10.1364/JOSAA.425668
M3 - Article
AN - SCOPUS:85109423146
SN - 1084-7529
VL - 38
SP - 992
EP - 1002
JO - Journal of the Optical Society of America A: Optics and Image Science, and Vision
JF - Journal of the Optical Society of America A: Optics and Image Science, and Vision
IS - 7
ER -