4.1.1. Simulations Based on Different Combinations of Features

Among the four proposed features, *v* is an inherent property varying with vessels and *L* reflects the overall movement trend of the track segment, while both *k* and *R* reflect the degree of deviation and regression of vessels under the interference of wind and waves, respectively. We carried out simulations on the basis of different combinations of features. As is shown in Table 1, *k* is more effective than *R* in characterizing the features of track segments, and the correct association probability reaches the highest when they work together, which illustrates that *k* and *R* can reinforce one another in reflecting the degree of deviation and regression of vessels. Therefore, the proposed method in this paper selects all four of these features to work together for better association performance.

**Table 1.** Statistical results of track segment association based on different features (%).


4.1.2. Simulations Based on Different Machine Learning Methods

We carried out simulations based on different machine learning methods to show the performance of an ELM. As is shown in Table 2, the ELM has the fastest speed and the highest accuracy compared with the back propagation (BP) network and the SVM. Moreover, the ELM is more efficient than the BP and the SVM. Hence, we selected the ELM to combine with the IMMEKF in the proposed method in this paper to achieve long-term continuous tracking of multiple targets.


**Table 2.** Statistical results of track segment association based on different machine learning methods. BP: back propagation; SVM: support vector machine.
