Abstract
With the development of various advanced sensors, and some sensing technologies are not mature, so that measurement information was being uncertain, incomplete. This paper adopts an intelligent fusion algorithm with Rough Set for reduction of the attribute set and target set for the raw data from various sensors. Consequently the noise and redundancy will be reduced in sampling. Then constructs information prediction system of SVM according to the preprocessing information structure, and solves the problem of multisensor data fusion in the situation of small sample and uncertainty. In order to get the optimal fusion accuracy, it uses PSO for fusion parameters. To make operation faster and increase the accuracy of the fusion, a feature selection process with PSO is used in this paper to optimize the fusion accuracy by its superiority of optimal search ability.
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Zhu, P., Xu, B., Lu, M. (2013). An Intelligent Fusion Algorithm for Uncertain Information Processing. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_35
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DOI: https://doi.org/10.1007/978-3-642-38715-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
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