Adaptive Distributed Kalman Filtering with Wind Estimation for Astronomical Adaptive Optics
Résumé
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 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 to update the Kalman filter itself with such a 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, promising, and the additional computational cost with respect to the distributed filter is negligible. Comparison with a previously published SLOpe Detection And Ranging (SLODAR) wind profiler is 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.
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