The controllability and maneuverability of an Autonomous Underwater Vehicle (AUV) in practical applications need to be properly validated and assessed before the prototype is finalized for manufacturing. With regard to mathematical system model of the AUV, hydrodynamic coefficients have a dominant effect on the quality of vehicle pre-testing and evaluation, which is crucial to be estimated with adequate accuracy to curb the uncertainty from modeling simplifications. The standard time domain discrete Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms are two promising numerical approximation approaches for hydrodynamic identification technique with favourable computational complexity and acceptable estimation precision. In this paper, a study with respect to time domain discrete optimized UKF (OUKF) algorithm based on recursive tuning rule and update gradient descent lemma for a typical prototype known as NPS AUV II will be proposed to enhance adaptability and prediction performance of the identification approach with appropriate verification. In addition, Auto Regressive Moving Average (ARMA) noisy model needs to be included into three Kalman Filter (KF) algorithms to further improve the estimation precision and perturbations inhibiting performance. Pre-processing numerical model validation and non-dimensional viscous linear damping coefficient identification based on three KF algorithms will be implemented respectively to provide an assistant pre-assessment for the vehicle. In accordance with comparable outputs and estimation-experiment errors, the OUKF identification algorithm is certified to be more precise and superior compared with EKF and UKF approaches in the presence of ARMA noisy model.

在原型最终定型用于制造之前，需要对在实际应用中的自动水下航行器（AUV）的可控性和可操纵性进行适当的验证和评估。关于AUV的数学系统模型，流体动力系数对车辆的预测试和评估质量起主要作用，这对于以足够的精度进行估算以抑制建模简化带来的不确定性至关重要。标准时域离散扩展卡尔曼滤波器（EKF）和无味卡尔曼滤波器（UKF）算法是用于流体力学识别技术的两种有希望的数值逼近方法，具有良好的计算复杂性和可接受的估计精度。在本文中，将针对基于NPS AUV II的典型原型，基于递归调整规则和更新梯度下降引理对时域离散优化UKF（OUKF）算法进行研究，以提高识别方法的适应性和预测性能，并进行适当的验证。另外，自回归移动平均（ARMA）噪声模型需要包含在三种卡尔曼滤波器（KF）算法中，以进一步提高估计精度和扰动抑制性能。基于三种KF算法的预处理数值模型验证和无量纲粘性线性阻尼系数识别将分别实现，以提供车辆的辅助预评估。根据可比的输出和估计实验误差，