Imaging through turbulence using adaptive optics (AO) is limited by scintillation, even with perfect wavefront sensing
and reconstruction. Such errors can be mitigated in closed loop by multi-conjugate AO systems consisting of two phase
correctors, each of which is driven by a pair of wavefront sensor phase measurements, along with an internal probe
beam that samples the beam train along a common path while propagating in the opposite direction as the external
signal beam or beacon wavefront that samples the turbulence. Such decentralized architectures avoid not only direct
measurement and feedback of irradiance but also intensive and/or highly coupled nonlinear control algorithms in favor
of simpler, more conventional linear control laws. They also admit linear dynamical-systems modeling in the spatial-frequency
domain. In this framework, coupled scintillation and servo-lag wave correction errors induced by turbulence
are here predicted parametrically by scalably filtering and numerically integrating power spectral density profiles. The
role of regularization is explored, and comparisons to previous nonlinear wave-optic simulation results are made.
Imaging through turbulence using adaptive optics is limited by scintillation, even with perfect wavefront sensing and reconstruction. Such errors can be mitigated in closed loop by multi-conjugate adaptive optics systems consisting of two phase correctors, each of which is driven by a pair of wavefront sensor phase measurements, along with an internal probe beam that samples the beam train along a common path while propagating in the opposite direction as the external signal beam or beacon wavefront that samples the turbulence. With this arrangement, not only direct measurement and feedback of irradiance but also intensive and/or highly coupled nonlinear control algorithms can be avoided in favor of more conventional, simple, decentralized linear control laws. Linear stability analysis of such systems is feasible in spatial frequency domain, and nonlinear wave-optic simulations in time domain suggest that, given sufficient temporal bandwidth, rejection of combined phase and amplitude disturbances can be enhanced by a factor of two or more (as quantified by error variances or Strehl ratio logarithms). Previous studies by other authors are extended using simplified regularization methods.
Spherical-wave scintillation is shown to impose multi-conjugate adaptive optics (MCAO) correctability limitations that are independent of wavefront sensing and reconstruction. Residual phase and log-amplitude variances induced by scintillation in weak turbulence are derived using (diffraction-based) <i>diffractive MCAO </i>spatial filters or (diffraction-ignorant) <i>geometric MCAO</i> proportional gains as linear open-loop control parameters. In the case of Kolmogorov turbulence, expressions involving the Rytov variance and/or weighted <i>C<sub>n</sub><sup>2</sup></i> integrals apply. Differences in performance between diffractive MCAO and geometric MCAO resemble chromatic errors. Optimal corrections based on least squares imply irreducible performance limits that are validated by wave-optic simulations.
Low-order turbulence effects dominate the random irradiance fluctuations in a weakly-scintillated Gaussian beam subject to a certain
set of initial conditions, leading to a natural departure from log normal irradiance behavior. This departure conflicts with earlier
theoretical studies of weakly scintillated beams which have traditionally assumed log normal behavior. The dominance of low order
effects leads to an increase in the theoretical scintillation and probability of fade relative to predictions based on the assumption of
log normal behavior. This paper recounts a detailed derivation of a low order turbulence model that successfully captures the non-log
normal behavior, and reviews theoretical scintillation and probability of fade predictions that follow from the model.
Much recent attention has been paid to wavefront sensing by phase-diverse phase retrieval (PDPR): estimating the wavefront in an exit pupil based on point-spread function measurements that incorporate known additional aberrations. The Next-Generation Space Telescope (NGST), for example, is expected to rely on this technology. This paper studies narrowband PDPR via "point-by-point" reconstruction, which estimates the phase at each sampled point in the pupil plane without using basis functions. The performance of an iterative, point-by-point phase diversity (PD) algorithm is demonstrated on data from an NGST-oriented wavefront sensing and control testbed as well as simulated data. Encouraging performance is exhibited in simulation and on experimental images in the presence of mild, continuous aberrations; however, in the presence of larger, discontinuous aberrations the experimental performance is poorer. The estimation algorithm is also used to compute Cramer-Rao bounds (CRBs) for a simulated PDPR problem and to analyze their sensitivity to system parameters such as field-of-view, wavelength, and the amount of focus diversity.