基于质量-弹簧-阻尼等效模型的海况与船舶参数联合估计
Joint Estimation of Sea State and Vessel Parameters Using a Mass-Spring-Damper Equivalence Model
AI总结：
本文提出基于质量-弹簧-阻尼等效模型的联合估计方法，用于实时估计海况和船舶参数，无需先验传递函数知识，通过动态建模和卡尔曼滤波提升估计精度。
摘要：
实时海况估计对于船舶制造和海上安全应用至关重要。传统方法依赖于准确的波-船体传递函数来从船载传感器估计波谱。相比之下，我们的方法在不需要先验传递函数知识的情况下联合估计海况和船舶参数，这可能不可用或变化。我们使用伪质量-弹簧-阻尼模型对波-船体系统进行建模，并开发了系统的动态模型。该方法允许将波激励递归建模为时间变化输入，放松了先前工作的常数输入假设。我们推导了统计一致的过程噪声协方差，并实现了平方根立方体卡尔曼滤波器用于传感器数据融合。进一步，我们推导了后验Cramer-Rao下界以评估估计器性能。广泛的蒙特卡洛模拟和来自高保真验证模拟器的数据证实，估计的波谱与假设完全传递函数知识的方法相匹配。
英文摘要：
Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works' assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
