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Optical Engineering

Simultaneous perturbation method for processing magnetospheric images
Author(s): Daniel C. Chin
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Paper Abstract

Extracting a multivariate nonlinear physical model from a set of satellite images is considered as a multivariate nonlinear regression problem. Multiple local solutions often prevent gradient type algorithms from obtaining global optimal solutions. A method of solving this problem is presented based on the simultaneous perturbation stochastic approximation (SPSA) algorithm. The method is applied to a problem of estimating the distribution of energetic ion populations in the magnetosphere from global images of the magnetosphere. The approach uses multiple objective functions: single image errors and the summation of square image errors. The algorithm is demonstrated on simulated energetic- neutral atom (ENA) images. Within a reasonable number of function evaluations, the process converges and reconstructs the images with a mean square error less than or equal to 0.1% of the original image. Also, the SPSA method is compared with results obtained from simulated annealing (SAN) in a single objective function setting. In the comparison study, SPSA has a 3:1 advantage over SAN in both accuracy and efficiency measures.

Paper Details

Date Published: 1 April 1999
PDF: 6 pages
Opt. Eng. 38(4) doi: 10.1117/1.602104
Published in: Optical Engineering Volume 38, Issue 4
Show Author Affiliations
Daniel C. Chin, Johns Hopkins Univ. (United States)


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