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Sensors 2014, 14(10), 19669-19686;

An Integrated Model for Robust Multisensor Data Fusion

School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China
Author to whom correspondence should be addressed.
Received: 3 September 2014 / Revised: 4 October 2014 / Accepted: 17 October 2014 / Published: 22 October 2014
(This article belongs to the Section Sensor Networks)
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This paper presents an integrated model aimed at obtaining robust and reliable results in decision level multisensor data fusion applications. The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine. It includes three main improvement aspects: a mass constructing algorithm to build reasonable basic belief assignments (BBAs); an evidence synthesis method to get a comprehensive BBA for an information source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM). Compared to some universal classification methods, the proposed one can be directly applied in multisensor data fusion applications, but not only for conventional classifications. Experimental results demonstrate that the proposed model is able to yield robust and reliable results in multisensor data fusion problems. In addition, this paper also draws some meaningful conclusions, which have significant implications for future studies. View Full-Text
Keywords: multisensors; data fusion; Dempster-Shafer theory; extreme learning machine multisensors; data fusion; Dempster-Shafer theory; extreme learning machine

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Shen, B.; Liu, Y.; Fu, J.-S. An Integrated Model for Robust Multisensor Data Fusion. Sensors 2014, 14, 19669-19686.

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