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Optical neural network system for pose determination of spinning satellitesAn optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.
Document ID
19910052182
Document Type
Conference Paper
Authors
Lee, Andrew
(Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Casasent, David
(Carnegie Mellon University Pittsburgh, PA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1990
Subject Category
Cybernetics
Meeting Information
Hybrid Image and Signal Processing II(Orlando, FL)
Distribution Limits
Public
Copyright
Other
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