An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

作者：de Cos Juez Francisco J; Sanchez Lasheras Fernando; Roqueni Nieves; O**orn James

来源：Sensors (Switzerland), 2012, 12(7): 8895-8911.

DOI：10.3390/s120708895

• MOAO
• adaptive
• optics
• neural
• networks
• reconstructor
• Zernike

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%26apos;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).

• 出版日期2012-7