Computational Methods for Atmospheric Optics

Abstract

The development of efficient non-negatively constrained optimization algorithms for image deblurring. This includes a new pre-conditioner based on a. sparse approximation to the blurring operator. The development of efficient pre-conditioners for the joint phase and object estimation problem in phase diversity. These pre-conditioners were based on the Hessian of the (quadratic) regularization terms. This paper also contains a careful numerical study and comparison of trust region vs. limited memory BFGS methods for the numerical solution to optimization problems arising in phase diversity estimation. Data for this study was obtained from the US Air Force Maui Spate Surveillance Complex in collaboration with Dr. David Tyler. The development of obtained preconditioned conjugate gradient schemes for volume refractive index (turbulence) estimation These schemes make, efficient use of the layered structure of the atmospheric turbulence profiles. This layered structure gave rise to block-structured matrices. We employed a block analogue of symmetric Gauss-Seidel iteration as our multi-grid smoother.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2002
Accession Number
ADA409646

Entities

People

  • Curtis R. Vogel

Organizations

  • Montana State University

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Adaptive Optics
  • Air Force
  • Algorithms
  • Atmospheric Motion
  • Computational Science
  • Convolution Integrals
  • Earth Sciences
  • Geography
  • Image Processing
  • Inverse Problems
  • Mathematics
  • Optics
  • Optimization
  • Refractive Index
  • Research Facilities
  • Surveillance
  • Turbulence

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.
  • Operations Research