Optimal wave-front reconstruction strategies for multiconjugate adaptive optics (original) (raw)
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Efficient phase estimation for large-field-of-view adaptive optics
Optics Letters, 1999
We propose a maximum a posteriori -based estimation of the turbulent phase in a large f ield of view (FOV) to overcome the anisoplanatism limitation in adaptive optics. We show that, whatever the true atmospheric prof ile, a small number of equivalent layers (two or three) is required for accurate restoration of the phase in the whole FOV. The implications for multiconjugate adaptive optics are discussed in terms of the number and conjugated heights of the deformable mirrors. The number of guide stars required for wave-front measurements in the f ield is also discussed: three (or even two) guide stars are sufficient to produce good performance.
Journal of the Optical Society of America A, 2009
The performances of various estimators for wavefront sensing applications such as adaptive optics (AO) are compared. Analytical expressions for the bias and variance terms in the mean squared error (MSE) are derived for the minimum-norm maximum likelihood (MNML) and the maximum a posteriori (MAP) reconstructors. The MAP estimator is analytically demonstrated to yield an optimal trade-off that reduces the MSE, hence leading to a better Strehl ratio. The implications for AO applications are quantified thanks to simulations on 8-m-and 42-m-class telescopes. We show that the MAP estimator can achieve twice as low MSE as MNML methods do. Large AO systems can thus benefit from the high quality of MAP reconstruction in O͑n͒ operations, thanks to the fast fractal iterative method (FrIM) algorithm (Thiébaut and Tallon, submitted to J. Opt. Soc. Am. A).
Journal of the Optical Society of America A, 2010
Real-time turbulence profiling is necessary to tune tomographic wavefront reconstruction algorithms for widefield adaptive optics (AO) systems on large to extremely large telescopes, and to perform a variety of image post-processing tasks involving point-spread function reconstruction. This paper describes a computationally efficient and accurate numerical technique inspired by the slope detection and ranging (SLODAR) method to perform this task in real time from properly selected Shack-Hartmann wavefront sensor measurements accumulated over a few hundred frames from a pair of laser guide stars, thus eliminating the need for an additional instrument. The algorithm is introduced, followed by a theoretical influence function analysis illustrating its impulse response to high-resolution turbulence profiles. Finally, its performance is assessed in the context of the Thirty Meter Telescope multi-conjugate adaptive optics system via end-to-end wave optics Monte Carlo simulations.
Advanced Maui Optical and Space Surveillance Technologies Conference, 2007
Atmospheric turbulence degrades the resolution of images of space objects beyond that predicted by diffraction alone. Adaptive optics telescopes have been widely used for compensating these effects, but as users seek to extend the envelopes of operation of adaptive optics telescopes to more demanding conditions, such as daylight operation and operation at low elevation angles, the level of compensation provided will degrade. We have been investigating the use of advanced wave front reconstructors and post detection image reconstruction to overcome the effects of turbulence on imaging systems in these more demanding scenarios. In this paper we show results comparing the optical performance of the exponential reconstructor, the least squares reconstructor, and the stochastic parallel gradient descent algorithm in a closed loop adaptive optics system using a conventional continuous facesheet deformable mirror and a Hartmann sensor. The performance of these reconstructors has been evaluated under a range of source visual magnitudes, and zenith angles up to 67 degrees. We have also simulated satellite images, and applied speckle imaging, multiframe blind deconvolution algorithms, and deconvolution algorithms that presume the average point spread function is known to compute object estimates.
Optimal control law for classical and multiconjugate adaptive optics
Journal of the Optical Society of America A, 2004
Classical adaptive optics (AO) is now a widespread technique for high-resolution imaging with astronomical ground-based telescopes. It generally uses simple and efficient control algorithms. Multiconjugate adaptive optics (MCAO) is a more recent and very promising technique that should extend the corrected field of view. This technique has not yet been experimentally validated, but simulations already show its high potential. The importance for MCAO of an optimal reconstruction using turbulence spatial statistics has already been demonstrated through open-loop simulations. We propose an optimal closed-loop control law that accounts for both spatial and temporal statistics. The prior information on the turbulence, as well as on the wave-front sensing noise, is expressed in a state-space model. The optimal phase estimation is then given by a Kalman filter. The equations describing the system are given and the underlying assumptions explained. The control law is then derived. The gain brought by this approach is demonstrated through MCAO numerical simulations representative of astronomical observation on a 8-m-class telescope in the near infrared. We also discuss the application of this control approach to classical AO. Even in classical AO, the technique could be relevant especially for future extreme AO systems.
Dual-conjugate wavefront generation for adaptive optics
Optics …, 2000
We present results of the isoplanatic performance of an astronomical adaptive optics system in the laboratory, by using a dual layer turbulence simulator. We describe how the performance of adaptive correction degrades with off-axis angle. These experiments demonstrate that it is now possible to produce quantifiable multi-layer turbulence in the laboratory as a precursor to constructing multi-conjugate adaptive optics.
Journal of the Optical Society of America A, 2010
Extremely Large Telescopes (ELTs) are very challenging with respect to their adaptive optics (AO) requirements. Their diameters and the specifications required by the astronomical science for which they are being designed imply a huge increment in the number of degrees of freedom in the deformable mirrors. Faster algorithms are needed to implement the real-time reconstruction and control in AO at the required speed. We present the results of a study of the AO correction performance of three different algorithms applied to the case of a 42-m ELT: one considered as a reference, the matrix-vector multiply (MVM) algorithm; and two considered fast, the fractal iterative method (FrIM) and the Fourier transform reconstructor (FTR). The MVM and the FrIM both provide a maximum a posteriori estimation, while the FTR provides a least-squares one. The algorithms are tested on the European Southern Observatory (ESO) end-to-end simulator, OCTOPUS. The performance is compared using a natural guide star single-conjugate adaptive optics configuration. The results demonstrate that the methods have similar performance in a large variety of simulated conditions. However, with respect to system misregistrations, the fast algorithms demonstrate an interesting robustness.
New technique for wave-front reconstruction in optical telescopes
Journal of the Optical Society of America A, 1997
A new approach to wave-front sensing for adaptive optics systems is presented. The method, which explicitly accounts for measurement errors, is based on a phase-retrieval algorithm whose performance with respect to trapping and stagnation problems is improved by use of a suitable mathematical formulation. The algorithm allows the determination of the expansion coefficients of the wave front in terms of Zernike polynomials. A numerical analysis shows the effectiveness of the algorithm even when the maximum optical path disturbance value is greater than two wavelengths and the measurement error is quite significant.
Fast minimum variance wavefront reconstruction for extremely large telescopes
Journal of the Optical Society of America A, 2010
We present a new algorithm, FRiM (FRactal Iterative Method), aiming at the reconstruction of the optical wavefront from measurements provided by a wavefront sensor. As our application is adaptive optics on extremely large telescopes, our algorithm was designed with speed and best quality in mind. The latter is achieved thanks to a regularization which enforces prior statistics. To solve the regularized problem, we use the conjugate gradient method which takes advantage of the sparsity of the wavefront sensor model matrix and avoids the storage and inversion of a huge matrix. The prior covariance matrix is however non-sparse and we derive a fractal approximation to the Karhunen-Loève basis thanks to which the regularization by Kolmogorov statistics can be computed in O(N) operations, N being the number of phase samples to estimate. Finally, we propose an effective preconditioning which also scales as O(N) and yields the solution in 5-10 conjugate gradient iterations for any N. The resulting algorithm is therefore O(N). As an example, for a 128 × 128 Shack-Hartmann wavefront sensor, FRiM appears to be more than 100 times faster than the classical vector-matrix multiplication method.
Adaptive Optics for Extremely Large Telescopes 4 – Conference Proceedings , 1 ( 1 )
2016
In this paper, we will present new compression algorithms to determine optimal layer heights and turbulence weights for the tomographic reconstruction in wide field AO systems. Among other approaches, a new compression method based on discrete optimization of collecting atmospheric layers to subgroups is discussed. Furthermore, studies of the influence of layer heights and cn-profiles on the reconstruction quality for di erent reconstruction algorithms and atmospheric profiles will be shown. Our comparison suggests that reconstructions on fewer atmospheric layers yield comparable quality with lower computational e ort, if an appropriate compression algorithm is used. The numerical results were obtained on the ESO end-to-end simulation tool OCTOPUS.