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Proceedings Paper

Optical neural network system for pose determination of spinning satellites
Author(s): Andrew John Lee; David P. Casasent
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Paper Abstract

An 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 sateffites 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 (sateffite) 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 effiptical path of a part in image space the 3-D pose of the satellite is determined. Digital simulation results using this algorithm are presented for various sateffite poses and lighting conditions. 1

Paper Details

Date Published: 1 September 1990
PDF: 12 pages
Proc. SPIE 1297, Hybrid Image and Signal Processing II, (1 September 1990); doi: 10.1117/12.21326
Show Author Affiliations
Andrew John Lee, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 1297:
Hybrid Image and Signal Processing II
David P. Casasent; Andrew G. Tescher, Editor(s)

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