It is essential for a safe and cost-efficient marine operation to improve the knowledge about the real-time onboard vessel conditions. This paper proposes a novel algorithm for simultaneous tuning of important vessel seakeeping model parameters and sea state characteristics based on onboard vessel motion measurements and available wave data. The proposed algorithm is fundamentally based on the unscented transformation and inspired by the scaled unscented Kalman filter, which is very computationally efficient for large dimensional and nonlinear problems. The algorithm is demonstrated by case studies based on numerical simulations, considering realistic sensor noises and wave data uncertainties. Both long-crested and short-crested wave conditions are considered in the case studies. The system state of the proposed tuning framework consists of a vessel state vector and a sea state vector. The tuning results reasonably approach the true values of the considered uncertain vessel parameters and sea state characteristics, with reduced uncertainties. The quantification of the system state uncertainties helps to close a critical gap towards achieving reliability-based marine operations.

对于安全和具有成本效益的海上作业来说，至关重要的是要提高有关实时船上船舶状况的知识。本文提出了一种基于船上船舶运动测量和可用海浪数据同时调节重要船舶航海模型参数和海况特征的新算法。所提出的算法从根本上基于无味变换，并受到可缩放的无味卡尔曼滤波器的启发，该算法在处理大维和非线性问题时非常有效。通过基于数值模拟的案例研究证明了该算法，其中考虑了实际的传感器噪声和波数据不确定性。案例研究同时考虑了长波和短波的情况。所提出的调整框架的系统状态由船状态向量和海状态向量组成。调整结果合理地接近了所考虑的不确定船只参数和海况特征的真实值，并减少了不确定性。系统状态不确定性的量化有助于弥合实现基于可靠性的海上作业的关键差距。