Abstract
This paper mainly concentrates on the problem of tracking multiple targets in the noisy environment. To better recognize the eccentric target in a specific environment, one proposed objective function gets the target’s shape in the subgraph. Inspired by particle swarm optimization, the proposed algorithm of tracking multiple targets adaptively modifies the covered radii of each subgroup in terms of the minimum distances among the subgroups, and successfully tracks the conflicting targets. The theoretic results as well as the experiments on tracking multiple ants indicate that this efficient method has successfully been applied to the complex and changing practical systems.
The work is supported by National Nature Science Foundation of China under Grant 60974046, 61011130163 and 61004059. And this work is also supported by Program for New Century Excellent Talents in University.
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Liu, J., Ma, H., Ren, X. (2011). Tracking Multiple Targets with Adaptive Swarm Optimization. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_20
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DOI: https://doi.org/10.1007/978-3-642-20525-5_20
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