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Proceedings Paper

Genetic algorithm for multiple-target-tracking data association
Author(s): Jean-Yves Carrier; John Litva; Henry Leung; Titus K. Y. Lo
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Paper Abstract

The heart of any tracking system is its data association algorithm where measurements, received as sensor returns, are assigned to a track, or rejected as clutter. In this paper, we investigate the use of genetic algorithms (GA) for the multiple target tracking data association problem. GA are search methods based on the mechanics of natural selection and genetics. They have been proven theoretically and empirically robust in complex space searches by the founder J. H. Holland. Contrary to most optimization techniques, which seek to improve performance toward the optimum, GA find near-optimal solutions through parallel searches in the solution space. We propose to optimize a simplified version of the neural energy function proposed by Sengupta and Iltis in their network implementation of the joint probability data association. We follow an identical approach by first implementing a GA for the travelling salesperson problem based on Hopfield and Tank's neural network research.

Paper Details

Date Published: 7 June 1996
PDF: 11 pages
Proc. SPIE 2739, Acquisition, Tracking, and Pointing X, (7 June 1996); doi: 10.1117/12.241914
Show Author Affiliations
Jean-Yves Carrier, McMaster Univ. (Canada)
John Litva, McMaster Univ. (Canada)
Henry Leung, Defense Research Establishment Ottawa (Canada)
Titus K. Y. Lo, McMaster Univ. (Canada)


Published in SPIE Proceedings Vol. 2739:
Acquisition, Tracking, and Pointing X
Michael K. Masten; Larry A. Stockum, Editor(s)

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