*3.2. Feature Extraction*

In order to sufficiently reflect the track segment features, we associate the tracks based on features extracted from track segments rather than comparing the distances of track segments point by point. To improve the tracking performance in a complex environment, more features should be extracted for track association. In this paper, we selected the average velocity, average curvature, ratio of the arc length to the chord length, and the wavelet coefficient as the feature vectors to train and test the ELM.

#### 3.2.1. Average Velocity (*v*)

Velocity as an inherent property varying with vessels can be reflected in the track segments, and thus we select the average velocity of the track segment as an important characteristic variable, which is defined as follows:

$$
\overline{v} = \frac{\sum\_{i=0}^{n} \sqrt{v\_{xi}^2 + v\_{yi}^2}}{n} \tag{24}
$$

where *vxi* is the velocity component of the vessel along the x axis, and *vyi* is the velocity component of the vessel along the y axis.
