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

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/ifmca-16.2017.39
ID  - Fan2017/03
ER  -