## 1 Introduction  
In recent years, the unmanned surface vessel (USV) has garnered increasing attention as an intelligent marine vessel widely used in various fields, such as military reconnaissance, marine scientific research, and environmental monitoring. In complex marine environments, the motion of the USV is a complex time-varying system. The models commonly used to describe the USV's maneuvering motion include the [PERSON] model [1], the Maneuvering Modeling Group model [2], and the Nomoto model [3]. Among them, the Nomoto model is relatively simple in structure and widely used due to its ease of application. The modeling methods mainly include model experiments, empirical formulas [4], computational fluid dynamics software calculations [5], and system identification [6]. Among these, system identification, as an important branch of modern control theory, offers advantages such as simplicity, efficiency, and ease of use, and has gradually become an essential research method for modeling USV motion. With the extensive research on identification algorithms in the field of marine engineering, various identification methods, such as the Kalman filter (KF), least squares (LS),and support vector machines (SVM), have been developed. Recent years have brought to attention filtering and state estimation paradigms in systems that exhibit rapidly changing states [7]. Many scholars have conducted research related to the identification of ship maneuverability. Aimed at the inconvenience of obtaining model parameters using the traditional experimental method, the parameter identification of the unmanned marine vehicle's (UMV) maneuvering model based on extended KF and SVM is studied to predict the maneuverability of UMV [8]. In order to further explore more efficient recognition algorithms to solve the problem of ship motion recognition modeling, a novel recognition algorithm for ship maneuvering modeling in three-dimensional space is proposed in [9] based on the recursive least squares (RLS) method, and it is validated by simulation experiments. In [10], a novel method, Gaussian process regression optimized by the genetic algorithm, is proposed for the nonparametric modeling of ship maneuvering motion, and the Mariner vessel and the container ship KCS are selected as case studies. A parameter identification method that merges experimentally monitored signals and physics-based simulation data is proposed, targeting the identification tasks in shaft seals, which are challenging due to the high-dimensional parameter space [11]. Meanwhile, [PERSON] [12] utilizes the LS method to determine the initial parameters of the ship's mathematical model, and then correlation and sensitivity analysis are conducted to select the key parameters in the Abkowitz model. The X-exchange technique is used to convert the ship's nonlinear response model into a linear form, and an improved LS identification algorithm is then proposed to estimate the unknown model parameters by [13]. A parameter identification scheme for the second-order nonlinear Nomoto model based on the square-root unscented Kalman filter is proposed by [PERSON] et al. [14], and the maneuvering parameters of the self-propelled model are determined based on simulation data. In [15], a novel parameter estimation algorithm is proposed that transforms a system with colored noise into two identification models with white noise, allowing for more accurate parameter estimates than existing related algorithms. The KF is considered for linear systems with unknown structural parameters in [16], and the classical KF is then employed to predict and update the states. The maximum correntropy Kalman filter is developed to address the issue of the rapid performance degradation of the KF when faced with non-Gaussian noise in [17]. In [18], a new adaptive fast desensitized Kalman filter algorithm and adaptive fast desensitized extended Kalman filter are proposed to adjust the sensitivity-weighting matrix in the desensitized Kalman filter.