Real-time adaptive optimization of wave-front reconstruction algorithms for closed-loop adaptive-optical systems

In recent years several methods have been presented for optimizing closed-loop adaptive-optical (AO) wave-front reconstruction algorithms. These algorithms, which can significantly improve the performance of AO systems, compute the 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 atmospheric statistics. This paper presents a method for adaptively optimizing the reconstructor of a closed-loop AO system in real 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 AO control algorithm attempts to compensate for signal processing latency by reconstructing the future value of the wave-front from a combination of past and current wave-front sensor measurements. For this case, the adaptive reconstruction algorithm yields Strehl ratios within a few per cent of those obtained by an optimal reconstructor derived from a priori knowledge of the strength of the turbulence and the velocity of the wind. This level of performance can be a dramatic improvement over the Strehls achievable with a conventional least squares reconstructor.