Shen Bo, Liu Yun, Fu Jun-Song
School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2014 Oct 22;14(10):19669-86. doi: 10.3390/s141019669.
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.
本文提出了一种集成模型，旨在决策级多传感器数据融合应用中获得稳健可靠的结果。所提出的模型基于Dempster-Shafer证据理论与极限学习机的结合。它包括三个主要改进方面：一种用于构建合理基本置信分配（BBA）的质量构建算法；一种从多个质量函数或专家处获取信息源综合BBA的证据合成方法；以及一种基于极限学习机（ELM）进行高精度决策的新方法。与一些通用分类方法相比，所提出的方法可直接应用于多传感器数据融合应用，而不仅限于传统分类。实验结果表明，所提出的模型能够在多传感器数据融合问题中产生稳健可靠的结果。此外，本文还得出了一些有意义的结论，对未来的研究具有重要意义。
