Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

The Algorithm of Multi-sensor Data Fusion Based on the Modified Reciprocal Fuzzy Neartude

Authors
Qiang Qu, Xinhua He, Weichao Zhang
Corresponding Author
Qiang Qu
Available Online June 2017.
DOI
https://doi.org/10.2991/icmia-17.2017.42How to use a DOI?
Keywords
Sensor, Data fusion, Fuzzy neartude, Distance neartude, Fuzzy weighted fusion, Fuzzy membership.
Abstract
In order to improve the accuracy and reliability of the multi-sensor data fusion, a new modified reciprocal fuzzy neartude based approach to calculate the weights of the fusion model is proposed. Through the research of the fusion model, it is found that the fuzzy neartude is more practical than fuzzy membership for the calculation of the weights. The fusion performance of the five types of fuzzy neartude is analyzed, and the reciprocal fuzzy neartude is proved to be one of the best for the resolution and mount of calculation. However, the neartude cannot suppress the singular data well. To address the problem, the reciprocal fuzzy neartude is modified. The simulation analysis shows that the modified reciprocal fuzzy neartude based approach can fuse the multi-sensor data with high accuracy and reliability.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-387-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/icmia-17.2017.42How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Qiang Qu
AU  - Xinhua He
AU  - Weichao Zhang
PY  - 2017/06
DA  - 2017/06
TI  - The Algorithm of Multi-sensor Data Fusion Based on the Modified Reciprocal Fuzzy Neartude
BT  - 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.42
DO  - https://doi.org/10.2991/icmia-17.2017.42
ID  - Qu2017/06
ER  -