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We present the details of predicting atmospheric turbulence by mining Zernikemoment data obtained from simulations as well as experiments. Temporallycorrelated optical wave-fronts were simulated such that they followedKolmogorov phase statistics. The wave-fronts reconstructed either by modal orzonal methods can be represented in terms of Zernike moments. The servo lagerror in adaptive optics is minimized by predicting Zernike moments in the nearfuture by using the data from the immediate past. It is shown statisticallythat the prediction accuracy depends on the number of past phase screens usedfor prediction and servo lag time scales. The algorithm is optimized in termsof these parameters for real time and efficient operation of the adaptiveoptics system. On an average, we report more than 3% improvement in thewave-front compensation after prediction. This analysis helps in optimizing thedesign parameters for sensing and correction in closed loop adaptive opticssystems.

Extrapolating Zernike Moments to Predict Future Optical Wave-fronts in Adaptive Optics Using Real Time Data Mining