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

TL;DR

This paper presents a data mining-based progressive prediction method for atmospheric turbulence in adaptive optics, significantly reducing servo bandwidth errors and improving wave-front correction accuracy.

Contribution

It introduces a novel progressive prediction approach using wave-front sensor data and segmentation algorithms to enhance real-time turbulence prediction in adaptive optics.

Findings

6% average improvement in wave-front correction

Effective turbulence prediction using data cube augmentation

Analysis of prediction performance under various parameters

Abstract

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. In this method, the data cube is augmented with the incoming wave-front sensor data and the newly formed data cube is used for further prediction. The statistics of the prediction method is studied under different experimental parameters like segment size, decorrelation timescales of turbulence and segmentation procedure. On an average, 6% improvement is seen in the wave-front correction after progressive prediction using data mining.