.png){kind=icon}
返回
An Integrated Model for Robust Multisensor Data Fusion
.png){kind=icon}
OA
AI
DOI：10.3390/s141019669.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
multisensors
data fusion
Dempster-Shafer theory
extreme learning machine
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
DesalinationOAAI
DesalinationIF0
OAAI分享收藏Information fusion techniques for change detection from multi-temporal remote sensing imagesINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processingSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏
查看更多内容