Journal of Positioning, Navigation, and Timing (J Position Navig Timing; JPNT)
Citation: Jeong, D. B., Choi, H.-T., & Ko, N. Y., 2023, Fault Detection in Automatic Identification System Data for Vessel Location Tracking, Journal of Positioning, Navigation, and Timing, 12, 257-269.
Journal of Positioning, Navigation, and Timing (J Position Navig Timing) 2023 September, Volume 12, Issue 3, pages 257-269. https://doi.org/10.11003/JPNT.2023.12.3.257
Received on 15 August 2023, Revised on 25 August 2023, Accepted on 29 August 2023, Published on 30 September 2023.
License: Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Da Bin Jeong1, Hyun-Taek Choi2, Nak Yong Ko1†
1Department of Electronic Engineering, Interdisciplinary Program in IT-Bio Convergence System, Chosun University, Gwangju 61452, Korea
2Advanced-Intelligent Ship Research Division, Korea Research Institute of Ships and Ocean Engineering (KRISO), Daejeon 34103, Korea
†Corresponding Author: E-mail, nyko@chosun.ac.kr; Tel: +82-62-230-7766 Fax: +82-62-608-5203
This paper presents a method for detecting faults in data obtained from the Automatic Identification System (AIS) of surface vessels. The data include latitude, longitude, Speed Over Ground (SOG), and Course Over Ground (COG). We derive two methods that utilize two models: a constant state model and a derivative augmented model. The constant state model incorporates noise variables to account for state changes, while the derivative augmented model employs explicit variables such as first or second derivatives, to model dynamic changes in state. Generally, the derivative augmented model detects faults more promptly than the constant state model, although it is vulnerable to potentially overlooking faults. The effectiveness of this method is validated using AIS data collected at a harbor. The results demonstrate that the proposed approach can automatically detect faults in AIS data, thus offering partial assistance for enhancing navigation safety.
fault detection, AIS, SOG, COG, location
Bakdi, A., Glad, I. K., Vanem, E., & Engelhardtsen, Ø. 2020, AIS-based multiple vessel collision and grounding risk identification based on adaptive safety domain, Journal of Marine Science and Engineering, 8, 5. https://doi. org/10.3390/jmse8010005
Burmeister, H. C., Bruhn, W. C., & Walther, L. 2015, Interaction of harsh weather operation and collision avoidance in autonomous navigation, TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 9, 31-40. https://doi.org/10.12716/1001.09.01.04
Burzigotti, P., Ginesi, A., & Colavolpe, G. 2012, Advanced receiver design for satellite‐based automatic identification system signal detection, International Journal of Satellite Communications and Networking, 30, 52-63. https://doi. org/10.1002/sat.1007
Forti, N., d’Afflisio, E., Braca, P., Millefiori, L. M., Carniel, S., et al. 2022, Next-gen intelligent situational awareness systems for maritime surveillance and autonomous navigation, Proceedings of the IEEE, 110, 1532-1537. https://doi.org/10.1109/JPROC.2022.3194445
Fournier, M., Hilliard, R. C., Rezaee, S., & Pelot, R. 2018, Past, present, and future of the satellite-based automatic identification system: Areas of applications (20042016), WMU Journal of maritime affairs, 17, 311-345. https://doi.org/10.1007/s13437-018-0151-6
Goudossis, A. & Katsikas, S. K. 2019, Towards a secure automatic identification system (AIS), Journal of Marine Science and Technology, 24, 410-423. https://doi. org/10.1007/s00773-018-0561-3
Jeong, D. B., Ko, N. Y., & Choi, H.-T. 2023, Vessel Location Tracking by Using Unscented Kalman Filter Implemented in Nonlinear Attitude Space, Journal of Institute of Control, Robotics and Systems, 29, 347-354. https:// doi.org/10.5302/J.ICROS.2023.22.8007
Kazimierski, W. & Stateczny, A. 2015, Radar and automatic identification system track fusion in an electronic chart display and information system, The Journal of Navigation, 68, 1141-1154. https://doi.org/10.1017/S0373463315000405
Mazzarella, F., Arguedas, V. F., & Vespe, M. 2015, Knowledgebased vessel position prediction using historical AIS data, In 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 06-08 October 2015, Bonn, Germany. https://doi.org/10.1109/SDF.2015.7347707
Papi, F., Tarchi, D., Vespe, M., Oliveri, F., Borghese, F., et al. 2015, Radiolocation and tracking of automatic identification system signals for maritime situational awareness, IET Radar, Sonar & Navigation, 9, 568-580. https://doi.org/10.1049/iet-rsn.2014.0292
Perera, L. P., Ferrari, V., Santos, F. P., Hinostroza, M. A., & Soares, C. G. 2015, Experimental evaluations on ship autonomous navigation and collision avoidance by intelligent guidance, IEEE Journal of Oceanic Engineering, 40, 374-387. https:// doi.org/10.1109/JOE.2014.2304793
Pietrzykowski, Z., Wołejsza, P., Nozdrzykowski, Ł., Borkowski, P., Banaś, P., et al. 2022, The autonomous navigation system of a sea-going vessel, Ocean Engineering, 261, 112104. https://doi.org/10.1016/j.oceaneng.2022.112104
Robards, M. D., Silber, G. K., Adams, J. D., Arroyo, J., Lorenzini, D., et al. 2016, Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review, Bulletin of Marine Science, 92, 75103. https://doi.org/10.5343/bms.2015.1034
Sun, X., Wang, G., Fan, Y., Mu, D., & Qiu, B. 2021, A formation autonomous navigation system for unmanned surface vehicles with distributed control strategy, IEEE Transactions on Intelligent Transportation Systems, 22, 28342845. https://doi.org/10.1109/TITS.2020.2976567
Wang, J., Lin, C., Ji, L., & Liang, A. 2012, A new automatic identification system of insect images at the order level, Knowledge-Based Systems, 33, 102-110. https://doi. org/10.1016/j.knosys.2012.03.014
Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X. 2019, How big data enriches maritime research–a critical review of Automatic Identification System (AIS) data applications, Transport Reviews, 39, 755-773. https://doi.org/10.1080 /01441647.2019.1649315
Conceptualization, H.-T. Choi and N. Y. Ko; methodology, N. Y. Ko, D. B. Jeong and H.-T. Choi; software, D. B. Jeong; validation, N. Y. Ko, H.-T. Choi and D. B. Jeong; formal analysis, N. Y. Ko and H.-T. Choi; investigation, N. Y. Ko, H.-T. Choi and D. B. Jeong; resources, N. Y. Ko, H.-T. Choi and D. B. Jeong; data curation, H.-T. Choi and D. B. Jeong; writing—original draft preparation, D. B. Jeong; writing— review and editing, N. Y. Ko and H.-T. Choi; visualization, D. B. Jeong; supervision, N. Y. Ko; project administration, N. Y. Ko and H.-T. Choi; funding acquisition, N. Y. Ko.
The authors declare no conflict of interest.