Recent advances in Maritime Autonomous Surface Ships (MASS) and e-Navigation technologies have increased the demand for decision support systems based on real-time maritime environmental information. In rough weather conditions, navigators are required to comprehensively assess wave conditions, atmospheric pressure, and wind variations. However, such decisions are still highly dependent on the experience and judgment of individual navigators. Therefore, quantitative decision support techniques are required for autonomous and remotely operated ships. This study proposes a rough weather navigation decision support system based on real-time wave observation data. The proposed system utilizes an X-band radar-based wave observation system to estimate significant wave height and employs a fuzzy inference engine with atmospheric pressure information to calculate heavy weather risk. In addition, a ship-tonnage compensation module is introduced to reflect the different impacts of rough weather according to vessel size. The calculated risk is used as an input to the Enhanced Recommend System under Rough Weather (RRW) algorithm. Based on the relative position of the vessel to a typhoon, the system recommends either Heave-to maneuvering in the dangerous semicircle or Scudding maneuvering in the navigable semicircle. Simulation results show that the proposed system appropriately estimates heavy weather risk according to wave height and atmospheric pressure conditions. Furthermore, the Enhanced RRW algorithm is activated only when the estimated risk exceeds a predefined threshold, thereby reducing unnecessary maneuvering actions. The proposed system can support safe navigation of conventional merchant ships and is expected to be applicable to decision support systems for MASS and remote ship operation environments.

Abstract
1. Introduction
2. Heavy Weather Maneuvering Analysis
3. Wave Observation System
4. Proposed Rough Weather Decision Support System
    4.1 Fuzzy-based Heavy Weather Risk Estimation
    4.2 Enhanced RRW Decision Support Algorithm
5. Simulation
6. Conclusion
7. Acknowledgements
References

• Taeho HONG(Division of Navigation & Information Systems, Mokpo National Maritime University, Korea) Corresponding author
• Geuntaek RO(Department of Maritime Transportation System, Mokpo National Maritime University, Korea)