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Nullifying the servo bandwidth errors improves the strehl ratio by asubstantial quantity in adaptive optics systems. An effective method forpredicting atmospheric turbulence to reduce servo bandwidth errors in real timeclosed loop correction systems is presented using data mining. Temporallyevolving phase screens are simulated using Kolmogorov statistics and used fordata analysis. A data cube is formed out of the simulated time series. Partialdata is used to predict the subsequent phase screens using the progressiveprediction method. The evolution of the phase amplitude at individual pixels issegmented by implementing the segmentation algorithms and prediction was madeusing linear as well as non linear regression. In this method, the data cube isaugmented with the incoming wave-front sensor data and the newly formed datacube is used for further prediction. The statistics of the prediction method isstudied under different experimental parameters like segment size,decorrelation timescales of turbulence and segmentation procedure. On anaverage, 6% improvement is seen in the wave-front correction after progressiveprediction using data mining.

Progressive Prediction of Turbulence Using Wave-Front Sensor Data in Adaptive Optics Using Data Mining