Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
© 2015 The Optical Society. Received 2 September 2015; revised 15 October 2015; accepted 15 October 2015; posted 15 October 2015 (Doc. ID 249257); published 17 November 2015. The authors gratefully acknowledge the support of the TMT collaborating institutions. They are the Association of Canadian Universities for Research in Astronomy (ACURA), the California Institute of Technology, the University of California, the National Astronomical Observatory of Japan, the National Astronomical Observatories of China and their consortium partners, and the Department of Science and Technology of India and their supported institutes. This work was supported as well by the Gordon and Betty Moore Foundation (GBMF), the Canada Foundation for Innovation (CFI), the Ontario Ministry of Research and Innovation (OMRI), the National Research Council of Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC), the British Columbia Knowledge Development Fund, the Association of Universities for Research in Astronomy (AURA), and the National Science Foundation (NSF). We also would like to thank Lewis Roberts (JPL) for providing the wind profile data. Funding: Association of Canadian Universities for Research in Astronomy (ACURA); California Institute of Technology; University of California; National Astronomical Observatory of Japan; National Astronomical Observatories of China; Department of Science and Technology of India; Gordon and Betty Moore Foundation (GBMF); Canada Foundation for Innovation (CFI); Ontario Ministry of Research and Innovation (OMRI); National Research Council of Canada; Natural Sciences and Engineering Research Council of Canada (NSERC); British Columbia Knowledge Development Fund; Association of Universities for Research in Astronomy (AURA); National Science Foundation (NSF); French National Institute of Applied Sciences (INSA) of Lyon.