无需已知波浪-船舶传递函数，实时联合估计海况与船体参数。
Joint Estimation of Sea State and Vessel Parameters Using a Mass-Spring-Damper Equivalence Model
- 用等效质量-弹簧-阻尼模型构建波浪-船舶动态系统。
- 在无先验传递函数条件下，估计的波谱与理想情况一致。
- 适合船舶自主导航与海上安全系统研发者参考。
实时海况估计对造船和航海安全至关重要。传统方法依赖精确的波浪-船舶传递函数，从船上传感器推断波谱。本文提出一种新方法，无需预先知晓传递函数即可联合估计海况与船舶参数，该函数可能缺失或随条件变化。通过将波浪-船舶系统建模为伪质量-弹簧-阻尼系统，构建动态模型，并将波浪激励视为时变输入，放宽了以往假设输入恒定的限制。推导出统计上一致的过程噪声协方差，采用平方根立方体卡尔曼滤波器实现传感器数据融合。进一步推导后验克拉美罗下界以评估估计器性能。大量蒙特卡洛仿真及高保真度验证模拟器数据表明，所估波谱与已知完整传递函数的方法结果高度一致。
原文摘要 · Abstract (English)
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.
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