Currently, marine ship targets are often identified directly through sensing targets. As a result, the potential value of massive spatiotemporal data has not been fully utilized. Because relevant maritime authorities have new requirements for understanding the situation of naval traffic flows and target identity behavior, obtaining necessary information about maritime targets at the data level and further understanding the ship movement patterns at the characteristic level are the focus of the current study. From the perspective of system design, this study proposes a method for obtaining a number of feature sets by mining the ship trajectory data and then identifying unknown targets. In the process of constructing the multi-characteristic sets, this study focuses on the importance of the distribution characteristics of ship channels for target identification. This study proposes an improved similarity measure—a trajectory rasterization model method. Experimental verification based on the measured data set shows that the system design can successfully fulfill the requirements for the identification of unknown targets and has a certain universality.

目前，海上船舶目标通常是通过传感目标直接识别的。因此，海量时空数据的潜在价值并未得到充分利用。由于相关海事主管部门对了解海上交通流量情况和目标识别行为有新的要求，因此在数据层面获取必要的海上目标信息，在特征层面进一步了解船舶运动模式是当前研究的重点。本研究从系统设计的角度，提出了一种通过挖掘船舶轨迹数据然后识别未知目标来获取多个特征集的方法。在构造多特征集的过程中，本研究侧重于船舶航道分布特征对目标识别的重要性。本研究提出了一种改进的相似性度量——轨迹光栅化模型方法。基于实测数据集的实验验证表明，该系统设计能够成功满足未知目标识别的要求，具有一定的通用性。