Genetic algorithm for multiple-target-tracking data association

Jean-Yves Carrier; John Litva; Henry Leung; Titus K. Y. Lo

doi:10.1117/12.241914

7 June 1996 Genetic algorithm for multiple-target-tracking data association

Jean-Yves Carrier, John Litva, Henry Leung, Titus K. Y. Lo

Proceedings Volume 2739, Acquisition, Tracking, and Pointing X; (1996) https://doi.org/10.1117/12.241914
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Jean-Yves Carrier, John Litva, Henry Leung, and Titus K. Y. Lo "Genetic algorithm for multiple-target-tracking data association", Proc. SPIE 2739, Acquisition, Tracking, and Pointing X, (7 June 1996); https://doi.org/10.1117/12.241914

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