Next Article in Journal
Maximum Likelihood-Based Iterated Divided Difference Filter for Nonlinear Systems from Discrete Noisy Measurements
Previous Article in Journal
Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
Sensors 2012, 12(7), 8895-8911; doi:10.3390/s120708895
Article

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

1,* , 2
,
1
 and
3
Received: 7 June 2012 / Revised: 18 June 2012 / Accepted: 26 June 2012 / Published: 27 June 2012
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [454 KB, uploaded 21 June 2014]   |   Browse Figures

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).
Keywords: MOAO; adaptive; optics; neural; networks; reconstructor; Zernike MOAO; adaptive; optics; neural; networks; reconstructor; Zernike
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Juez, F.J.C.; Lasheras, F.S.; Roqueñí, N.; Osborn, J. An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment. Sensors 2012, 12, 8895-8911.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert