An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment
Loading...
Date
2012
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).
Description
Keywords
MOAO, adaptive, optics, neural, networks, reconstructor, Zernike, OPTIMAL LINEAR-COMBINATIONS, ARTIFICIAL NEURAL-NETWORKS, ADAPTIVE-OPTICS, PERFORMANCE, HARDWARE, NONLINEARITIES, PRINCIPLES