Reliable maritime situational awareness is a key requirement for the development of intelligent ships. Radar and the Automatic Identification System (AIS) are among the most widely deployed navigation sensors on board ships, and the association and fusion of their data provide the foundation for intelligent perception. However, ship state measurement data are often characterized by asynchronous sampling, missing observations, and uncertainty, which make track association challenging. To address this problem, this paper proposes a multi-target track association algorithm based on Dynamic Time Warping (DTW) and Dempster-Shafer Theory (DST). First, multi-source track data are temporally aligned and represented using the DTW method. Then, multidimensional track features are fused based on DST. A two-stage association decision strategy is further constructed to obtain the globally optimal matching of heterogeneous sensor data under constrained conditions. A multi-target track simulation dataset is established according to the observation characteristics of radar and AIS targets, and the applicability of the proposed algorithm is evaluated under different scenarios. The algorithm is further validated using sea-trial data collected from the intelligent research and training vessel Xin Hong Zhuan of Dalian Maritime University. The results show that the F₁-score of the proposed algorithm exceeds 0.9 in the track association task, while the performance fluctuation across different scenarios is only 9.03%. These results demonstrate that the proposed method has high accuracy and robustness, providing technical support for multi-source perception data fusion and maritime situational awareness in intelligent ships.