과제정보
This study was sponsored by Daewoo Shipbuilding & Marine Engineering in 2022 and was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (Project Number:20213030020200, Project Name: Development of fully-coupled aero-hydro-servo-elastic-soil analysis program for offshore wind turbine system)
참고문헌
- Han, J. W. Lee, S. W., Joe, H. J. & Jin, E. S. (2022). A Study on the tracking of ARPA radar signal based on UK-PDAF and fusion with AIS data. Proceedings of KAOST 2022 in Jeju.
- Hu, Y., Zhang, A., Tian, W., Zhang, J., & Hou, Z. (2020). Multi-ship collision avoidance decision-making based on collision risk index. Journal of Marine Science and Engineering, 8(9), 640. https://doi.org/10.3390/jmse8090640
- Woo, J. (2018). Collision avoidance for an unmanned surface vehicle using deep reinforcement learning [Doctoral Thesis. Seoul National University Graduate School].
- Kim, D. Y., Park, G. K., & Kim, H. Y. (2014). A study on the ship information fusion with AIS and ARPA radar using by blackboard system. Journal of the Korean Institute of Intelligent Systems, 24(1), 16-21. https://doi.org/10.5391/JKIIS.2014.24.1.016
- Habtemariam, B., Tharmarasa, R., McDonald, M., & Kirubarajan, T. (2015). Measurement level AIS/radar fusion. Signal Processing, 106, 348-357. https://doi.org/10.1016/j.sigpro.2014.07.029
- Schreier, M.(2017). Bayesian environment representation, prediction, and criticality assessment for driver assistance systems. at - Automatisierungstechnik, 65(2), 151-152. https://doi.org/10.1515/auto-2016-0129
- Labbe, R. (2014). Kalman filter math. Kalman and bayesian filters in python, Chap 7(246), 4.