A genetic algorithm based multi-dimensional data association algorithm for multi-sensor--multi-target tracking

Authors:
G. Chen;L. Hong
Affiliations:
Department of Electrical Engineering, Wright State University Dayton, OH 45435, U.S.A.;Department of Electrical Engineering, Wright State University Dayton, OH 45435, U.S.A.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1997

Citing 2
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Sequential constraint augmentation with variable neighbourhood search for the multidimensional assignment problem

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Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking

Expert Systems with Applications: An International Journal
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Automation and Remote Control

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Abstract

The central problem in multitarget-multisensor tracking is the data association problem of partitioning the observations into tracks and false alarms so that an accurate estimate of true tracks can be found. The data association problem is formed as an N-dimensional (N-D) assignment problem, which is a state-of-the-art method and is NP-hard for N = 3 sensor scans. This paper proposes a new genetic algorithm for solving the above problem which is typically encountered in the application of target tracking. The data association capacities of the genetic algorithm have been studied in different environments, and the results are presented.