for closed-loop adaptive-optical systems

ABSTRACT In recent years several methods have been presented for optimizing closed-loop adaptive-optical (AU) wave-front re-construction algorithms. These algorithms, which can significantly improve the performance of AU systems, computethe reconstruction matrix using measured atmospheric statistics. Since atmospheric conditions vary on time scales of minutes, it becomes necessary to constantly update the reconstructor so that it adjusts to the changing atmosphericstatistics. This paper presents a method for adaptively optimizing the reconstructor of a closed-loop AU system inreal time. The method relies on recursive least squares techniques to track the temporal and spatial correlations of the turbulent wave-front. The performance of this method is examined for a sample scenario in which the AU control algorithm attempts to compensate for signal processing latency by reconstructing the future value of the wave-frontfrom a combination of past and current wave-front sensor measurements. For this case, the adaptive reconstructionalgorithm yields Strehl ratios within a few per cent of those obtained by an optimal reconstructor derived from apriori knowledge of the strength of the turbulence and the velocity of the wind. This level of performance can be adramatic improvement over the Strehls achievable with a conventional least squares reconstructor.Keywords: Wavefront reconstruction, adaptive optics, recursive least squares

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