Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

Research on Multi-Attribute Information Fusion for the Dynamic State of Inland River Vessels

Authors
Yaotian Fan, Xianzhang Xu, Chi Wang
Corresponding Author
Yaotian Fan
Available Online March 2017.
DOI
https://doi.org/10.2991/ifmca-16.2017.39How to use a DOI?
Keywords
Information Fusion; Adaptive Weighting; BP Neural Network; Kalman filtering
Abstract
The perception and discrimination of the traffic environment and the state of the ship are greatly hindered and restricted during the ship operation under complex weather conditions, especially in the inland river, port and other restricted waters. Aiming at the multi-source, multi-dimensional and heterogeneous information of ship borne sensors, the dynamic information fusion model is constructed to improve the perception and discrimination ability of the crew to the target vessels in inland waterway. Based on the analysis of the limitations and complementarities of the ship borne navigation equipment in the discrimination of the ship's dynamic state, the multi-source heterogeneous data fusion model is constructed of Kalman filtering, the adaptive weighted fusion method and neural network model, the data were simulated and measured in real conditions to verify the model and determine the system's reliability, stability and accuracy.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.39How 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  - Yaotian Fan
AU  - Xianzhang Xu
AU  - Chi Wang
PY  - 2017/03
DA  - 2017/03
TI  - Research on Multi-Attribute Information Fusion for the Dynamic State of Inland River Vessels
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 240
EP  - 247
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.39
DO  - https://doi.org/10.2991/ifmca-16.2017.39
ID  - Fan2017/03
ER  -