This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification.

中文翻译：

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基于数据滤波技术的多创新随机梯度算法的船舶操纵性参数识别算法和波峰模型

本文解决了识别船舶运动参数和波峰频率的问题。利用欧拉离散原理，我们建立了船波系统的具有外生输入的离散时间自回归移动平均模型（ARMAX）。此外，我们通过应用滤波技术和辅助模型识别思想，为系统开发了一种基于滤波的随机梯度算法。利用多创新点辨识理论，提出了一种基于滤波的多创新点随机梯度算法，以提高参数辨识的收敛速度和精度。人们发现这种方法比基于过滤的随机梯度算法更有效。仿真结果验证了该算法在参数识别方面的有效性。