Study on Correlating Multiple Attribute Information for the Dynamic State of Inland River Ships
- DOI
- 10.2991/icseee-16.2016.100How to use a DOI?
- Keywords
- Information Correlation; AIS; Shipborne Radar; Inland River Ships.
- Abstract
Based on the multi-sources, multi-dimension and isomerism of the ship-borne sensors, a model was constructed to correlate the ships' dynamic state information. It aims at enhancing the ability of inland river seaman on noticing and discriminating the target ship while the reliability of the model is improved at the same time. Since the vessels traffic in inland river is intensive and complex, it requires a more reliable information correlation based on the AIS and radar information fusion. This correlation model integrated the fuzzy Comprehensive method, BP neural network, grey theory and Euclidean method then used the redundancy technique to discriminate the correlation degrees and finally obtained the target radar information associated with the AIS data. An experiment was carried out to check the validity of the integrating model. It can not only increase the accuracy of information correlation but also provide the fundamental work on fusing AIS and radar data. In that case, this study is important as the theoretical references to guarantee the inland river navigation safety and enhance the traffic efficiency.
- Copyright
- © 2016, 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/).
Cite this article
TY - CONF AU - Yaotian Fan AU - Xianzhang Xu AU - Wang Wang AU - Chi Wang PY - 2016/12 DA - 2016/12 TI - Study on Correlating Multiple Attribute Information for the Dynamic State of Inland River Ships BT - Proceedings of the 2016 5th International Conference on Sustainable Energy and Environment Engineering (ICSEEE 2016) PB - Atlantis Press SP - 552 EP - 559 SN - 2352-5401 UR - https://doi.org/10.2991/icseee-16.2016.100 DO - 10.2991/icseee-16.2016.100 ID - Fan2016/12 ER -