Multisensor Data Fusion Based on Modified D-S Evidence Theory

DOI

10.2991/cmsa-18.2018.74How to use a DOI?

Keywords

Dempster-Shafer (D-S) evidence theory; multisensor data fusion; information gain; fuzzy preference relations

Abstract

Dempster-Shafer (D-S) evidence theory has been widely used in multisensor data fusion to deal with uncertain information. But unreasonable results may be produced by using D-S combination rule in the case of that data are conflicting with each other. This paper proposes a modified evidence combination method based on information gain and fuzzy preference relations. This method takes account of both historical data and real-time data by introducing the concepts of historical support and real-time support, so it can obtain more accurate results by using more effective information. In order to evaluate the performance of the proposed evidence combination method, an example of classifying the patient’s state by five vital signs is given in this paper. The simulation experiment shows that the proposed modified method achieves higher classification accuracy compared with other three data fusion methods.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yingming Zhou
AU  - Hongji Xu
AU  - Junfeng Sun
AU  - Lingling Pan
AU  - Baozhen Du
AU  - Min Chen
PY  - 2018/04
DA  - 2018/04
TI  - Multisensor Data Fusion Based on Modified D-S Evidence Theory
BT  - Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
PB  - Atlantis Press
SP  - 324
EP  - 327
SN  - 1951-6851
UR  - https://doi.org/10.2991/cmsa-18.2018.74
DO  - 10.2991/cmsa-18.2018.74
ID  - Zhou2018/04
ER  -