海洋船舶的轨迹估计算法

准确估计和预测海洋导航中机动船舶的轨迹是改善海上安全和保安的重要工具。因此,许多常规海洋导航系统和船舶交通管理和报告服务都为此目的配备了雷达设施。然而,船舶操纵轨迹的预测的准确性主要取决于船舶位置,速度和加速度的估计的良好性。因此,本研究提出了一个基于曲线运动模型的机动海洋船只模型,其基于用于相同目的的线性位置模型的测量。此外,还假定与白高斯噪声相关联的系统状态和测量模型。扩展卡尔曼滤波器被提出作为用于估计位置,速度和加速度的自适应滤波器算法,用于预测机动的海洋船舶轨迹。最后,提出的模型被实施,并获得成功的计算结果,在本研究中的海洋导航中的船舶操纵轨迹的预测。

Ocean Vessel Trajectory Estimation

The accurate estimation and prediction of the trajectories of maneuvering vessels in ocean navigation are important tools to improve maritime safety and security. Therefore, many conventional ocean navigation systems and Vessel Traffic Management Reporting Services are equipped with Radar facilities for this purpose. However, the accuracy of the predictions of maneuvering trajectories of vessels depends mainly on the goodness of estimation of vessel position, velocity and acceleration. Hence, this study presents a maneuvering ocean vessel model based on a curvilinear motion model with the measurements based on a linear position model for the same purpose. Furthermore, the system states and measurements models associated with a white Gaussian noise are also assumed. The Extended Kalman Filter is proposed as an adaptive filter algorithm for the estimation of position, velocity and acceleration that are used for prediction of maneuvering ocean vessel trajectory. Finally, the proposed models are implemented and successful computational results are obtained with respect to prediction of maneuvering trajectories of vessels in ocean navigation in this study.