Spatio-angular minimum-variance tomographic controller for multi-object adaptive-optics systems

Correia, Carlos; Jackson, Kate; Véran, Jean-Pierre; Andersen, David; Lardière, Olivier; Bradley, Colin

doi:10.1364/ao.54.005281

Cited by 23 publications

(18 citation statements)

References 31 publications

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“…Work on the LQG controller for full atmospheric disturbance compensation has also been done [10]. Different predictors, closely related to the Kalman filter, have allowed for filtering in real time and are now being used in laboratory testing as well as in on-sky testing [10][11][12][13][14][15]. The closely related data-driven H2 optimal controller [16][17][18] was also tested on-sky, for tip/tilt modes, showing a reduction in the temporal error.…”

Section: A Predictive Control In Adaptive Opticsmentioning

confidence: 99%

Impact of time-variant turbulence behavior on prediction for adaptive optics systems

2019

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For high contrast imaging systems, the time delay is one of the major limiting factors for the performance of the extreme adaptive optics (AO) sub-system and, in turn, the final contrast. The time delay is due to the finite time needed to measure the incoming disturbance and then apply the correction. By predicting the behavior of the atmospheric disturbance over the time delay we can in principle achieve a better AO performance. Atmospheric turbulence parameters which determine the wavefront phase fluctuations have time-varying behavior. We present a stochastic model for wind speed and model time-variant atmospheric turbulence effects using varying wind speed. We test a low-order, data-driven predictor, the linear minimum mean square error predictor, for a near-infrared AO system under varying conditions. Our results show varying wind can have a significant impact on the performance of wavefront prediction, preventing it from reaching optimal performance. The impact depends on the strength of the wind fluctuations with the greatest loss in expected performance being for high wind speeds.

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Section: A Predictive Control In Adaptive Opticsmentioning

confidence: 99%

Impact of time-variant turbulence behavior on prediction for adaptive optics systems

2019

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“…[11][12][13] Different varieties of the Kalman filter predictor, 14,15 which have allowed for filtering in real time, are now being used in laboratory testing 16 as well in on sky testing . 17 The data driven H 2 optimal control 18 was also tested on-sky 19 showing a reduction in the temporal error. Finally, adaptive predictors have been proposed 20 to track the time-varying behavior of atmospheric turbulence.…”

Section: Predictive Control In Adaptive Opticsmentioning

confidence: 99%

Performance of AO predictive control in the presence of non-stationary turbulence.

Kooten¹,

Doelman²,

Kenworthy³

2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

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Current high contrast imaging systems are limited at small angular separations, preventing the direct imaging of Earth-size exoplanets close to their host stars. One primary cause of this is the performance of the extreme adaptive optics (AO) system which is dominated by the servo-lag error at small angular separations. Prediction can be used to help reduce this servo-lag error. Most AO systems today do not use predictive controllers (integral controller), resulting in a phase correction that is proportional to the current measured wavefront and does not take into account the evolution of the wavefront phase fluctuations between measurement and correction. To improve upon this, we focus on the development of different predictive control strategies that estimate how the wavefront phase fluctuations evolve over a time horizon equal to the servo-lag. Moreover, the statistical properties of the turbulence phase are non-stationary (change in time) which needs to be included in the AO analysis for high contrast imaging systems. First, we show that the non-stationary properties of the atmosphere can be modeled using a von Karman covariance function which can incorporate variations in time of the Fried parameter, outer scale, and wind velocity. Using this new disturbance model of turbulence, the performance of three different predictors-based on a steady state, a recursive, and an adaptive linear-minimum-mean-squareestimator (LMMSE)-is tested under non-stationary conditions. We feed the prediction algorithms wavefront slopes from a 11-by-11 subaperture Shack-Hartmann wavefront sensor. A 97 actuator deformable mirror applies the predictor's phase correction. We present the latest results of our simulations and compare our predictors with the common integrator. We show that under our varying wind conditions, the root-mean-square wavefront error can increase by a factor of two for both the integrator and our predictors. In our simulations, we do not observe an increase in performance using an exponential forgetting factor adaptive LMMSE.

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“…We also tried to estimate wind speed and direction at each altitude during on-sky observations using a method presented in (Ono et al 2016), and tested new reconstruction algorithms (Correia et al 2015;Ono et al 2016), but in this paper we concentrate on retrieving the vertical profiles of C 2 N (h) and L0(h).…”

Section: Ravenmentioning

confidence: 99%

Statistics of turbulence parameters at Maunakea using the multiple wavefront sensor data of RAVEN

Ono

Correia

Andersen

et al. 2016

Mon. Not. R. Astron. Soc.

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Prior statistical knowledge of the atmospheric turbulence is essential for designing, optimizing and evaluating tomographic adaptive optics systems. We present the statistics of the vertical profiles of C 2 N and the outer scale at Maunakea estimated using a Slope Detection And Ranging (SLODAR) method from on-sky telemetry taken by RAVEN, which is a MOAO demonstrator in the Subaru telescope. In our SLODAR method, the profiles are estimated by a fit of the theoretical auto-and cross-correlation of measurements from multiple Shark-Haltmann wavefront sensors to the observed correlations via the non-linear Levenberg-Marquardt Algorithm (LMA), and the analytic derivatives of the spatial phase structure function with respect to its parameters for the LMA are also developed. The estimated profile has the median total seeing of 0.460 and large C 2 N fraction of the ground layer of 54.3 %. The C 2 N profile has a good agreement with the result from literatures, except for the ground layer. The median value of the outer scale is 25.5 m and the outer scale is larger at higher altitudes, and these trends of the outer scale are consistent with findings in literatures.

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Spatio-angular minimum-variance tomographic controller for multi-object adaptive-optics systems

Cited by 23 publications

References 31 publications

Impact of time-variant turbulence behavior on prediction for adaptive optics systems

Impact of time-variant turbulence behavior on prediction for adaptive optics systems

Performance of AO predictive control in the presence of non-stationary turbulence.

Statistics of turbulence parameters at Maunakea using the multiple wavefront sensor data of RAVEN

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