In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel's location and motion data collected through the Automatic Identification System (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures our approach demonstrates a superior tracking performance.

翻译：在海上监视中，区分正常与异常船舶运动模式对于及时发现潜在威胁至关重要。一旦检测到异常，需持续监控并跟踪这些船舶直至实施必要干预。为此，采用航迹关联算法，该算法利用包含船舶地理位置与运动参数的连续观测数据，将观测结果与对应船舶进行关联。这些连续观测数据固有的时空变化特性，使得传统多目标跟踪算法难以有效完成关联任务。此外，重叠航迹与数据缺失等问题的存在进一步增加了航迹跟踪的复杂性。针对上述挑战，本研究将航迹跟踪任务建模为多元时间序列问题，提出一种基于一维CNN-LSTM架构的航迹关联框架。该特殊神经网络架构能够同时捕捉连续观测数据中的空间模式与长期时间依赖关系。在训练过程中，模型自动学习并构建每艘船舶的航迹特征。训练完成后，该框架将自动识别系统（AIS）采集的船舶位置与运动数据作为输入，实时输出最可能的航迹结果。为评估方法性能，我们采用包含327艘特定海域船舶观测数据的AIS数据集，利用准确率、精确率、召回率及F1分数等标准性能指标进行评测。与其它竞争性神经网络架构相比，本方法展现出更优的跟踪性能。