1998
DOI: 10.1364/josaa.15.002745
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Stochastic parallel-gradient-descent technique for high-resolution wave-front phase-distortion correction

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Cited by 352 publications
(151 citation statements)
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“…Fig. 1 schematizes the model-free approach to wavefront control using a general "quality" metric [12]. In this schematized laser communication receiver system, the launched beam's wavefront phase is distorted by atmospheric turbulence encountered along its propagation path.…”
Section: Model-free Control For Adaptive Opticsmentioning
confidence: 99%
“…Fig. 1 schematizes the model-free approach to wavefront control using a general "quality" metric [12]. In this schematized laser communication receiver system, the launched beam's wavefront phase is distorted by atmospheric turbulence encountered along its propagation path.…”
Section: Model-free Control For Adaptive Opticsmentioning
confidence: 99%
“…Adaptive optical (AO) systems have been successfully applied to FSO to compensate the distorted wave front [3][4][5][6][7][8][9]. Based on the phase-conjugation principle, traditionally there are two main branches.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore the typically used sensors will fall short when high speed of detection and correction are needed, to implement fast adaptive optics in the FSO system. The other branch is the wave-front sensorless optimization method [3,9], which aims to optimize the performance metrics of the received laser, such as Strehl ratio (SR), root mean square (RMS) and image sharpness functions, etc. The AO system searches for the suitable voltage to control the DM to optimize the performance metric.…”
Section: Introductionmentioning
confidence: 99%
“…Optimization of the image quality metric was performed with the parallel stochastic-gradient-descent (PSGD) algorithm, which is based on estimating the gradient ∇ V Q(V ) by applying random perturbations to the input signals 18 . It is applicable to systems where there are many input signals and a performance metric that can be quantified, but no model information is available to relate the performance metric to the inputs.…”
Section: Deformable Mirror Shape Optimization and Controlmentioning
confidence: 99%
“…It is applicable to systems where there are many input signals and a performance metric that can be quantified, but no model information is available to relate the performance metric to the inputs. A derivation and justification of the approach can be found in 18 and the algorithm found in 19 is as follows.…”
Section: Deformable Mirror Shape Optimization and Controlmentioning
confidence: 99%