Progressive Prediction of Turbulence Using Wave- Front Sensor Data in Adaptive Optics Using Data Mining
Nullifying the servo bandwidth errors improves the strehl ratio by a substantial quantity in adaptive optics systems. An effective method for predicting atmospheric turbulence to reduce servo bandwidth errors in real time closed loop correction systems is presented using data mining. Temporally evolving phase screens are simulated using Kolmogorov statistics and used for data analysis. A data cube is formed out of the simulated time series. Partial data is used to predict the subsequent phase screens using the progressive prediction method. The evolution of the phase amplitude at individual pixels is segmented by implementing the segmentation algorithms and prediction was made using linear as well as non linear regression.