Many technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.

需要建立许多技术来实现自主船舶。特别是实时准确的状态估计是最重要的技术之一。在船舶和海洋工程领域，利用非线性卡尔曼滤波器进行状态估计已有很多研究。已经提出了几种用于非线性卡尔曼滤波器的方法。然而，对于其中应该应用哪种过滤器的验证还不够。因此，本研究旨在验证滤波器的选择，为滤波器的选择提供指导。研究了建模误差、观测噪声和机动类型对本研究中使用的无迹卡尔曼滤波器 ( UKF ) 和集成卡尔曼滤波器 ( EnKF )估计精度的影响。此外，还验证了是否可以实时进行过滤。结果表明，建模误差显着影响UKF和EnKF的估计精度。然而，观测噪声和机动类型并没有像建模误差那样产生影响。因此，我们得到了应根据所需计算时间不同地使用UKF和EnKF的指导方针。我们还发现，保持建模误差足够小对于提高估计精度至关重要。