An Integrated Model for Robust Multisensor Data Fusion

Sensors

Bo Shen¹  Yun Liu² 

[1] School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China;

关键词: multisensors;    data fusion;    Dempster-Shafer theory;    extreme learning machine;   

DOI  :  10.3390/s141019669

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.