## 4 Trajectory Prediction Model Based on AIS Data and ACDE-SVM
### Analysis of Experimental Process  
This study used the libsvm toolbox developed by Professor [PERSON] [44] of Taiwan University for modeling and simulations. AIS trajectories were extracted from 2,346,643 sets of AIS data from Tianjin Port waters in March 2015 using the algorithm described in Section 2.1. A certain trajectory of the ship with an MMSI of 538,004,758 was randomly selected as the research object; the trajectory was cleaned and missing values were interpolated. To satisfy the collision avoidance requirements, it was found that there may have been a collision risk with the target ship during maritime navigation. The AIS data of the target ship acquired in real time were used as the sample data to establish a prediction model, for which there may be a problem related to the small number of training samples. To simulate accurately, a part of the trajectory data was selected as the sample and 226 sets of AIS data were selected as the sample data. Figure 3 is ship trajectory with sample data sets. Among these, 200 groups were used as the training set and 26 groups were used as the test set to carry out simulation experiments. The sample data structure is summarized in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Serial** & **Ship** & **MMSI** & **Course/(\({}^{\circ}\))** & **Speed/(kcn)** & **Longitude/(\({}^{\circ}\))** & **Latitude/(\({}^{\circ}\))** & **Unix Time/(s)** \\ \hline
1 &
\begin{tabular}{c} STELLA \\ JADE \\ \end{tabular} & 538004758 & 279.1 & 11.9 & 120.019533 & 38.710583 & 1,426,284,921 \\ \multirow{2}{*}{2} & STELLA & 538004758 & 278.2 & 11.8 & 119.934217 & 38.720833 & 1,426,286,160 \\ \multirow{2}{*}{3} & STELLA & 538004758 & 278.5 & 11.8 & 119.886767 & 38.726333 & 1,426,286,873 \\ \multirow{2}{*}{4} & STELLA & 538004758 & 278.4 & 11.7 & 119.86145 & 38.729283 & 1,426,287,221 \\ \multirow{2}{*}{5} & STELLA & 538004758 & 278.3 & 11.7 & 119.838133 & 38.731967 & 1,426,287,560 \\ \multirow{2}{*}{6} & STELLA & 538004758 & 278.4 & 11.7 & 119.83266 & 38.732633 & 1,426,287,641 \\ \multirow{2}{*}{7} & STELLA & 538004758 & 278.5 & 11.7 & 119.831617 & 38.734850 & 1,426,287,920 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample data structure. Figure 3: Ship trajectory with sample data sets. The purple line refers to the Automatic Identification System (AIS) trajectory of ship. The purple hollow circle refers to the trajectory point of the selected trajectory segment and the arrow indicates the ship’s navigation direction. Input t\({}_{\text{i}}\) refers to the trajectory point of the current t\({}_{\text{i}}\) time and the past three times (t\({}_{\text{i}-1}\), t\({}_{\text{i}-2}\), t\({}_{\text{i}-3}\)) as the input data of the model and output t\({}_{\text{i}}\) refers to the trajectory of the next time t\({}_{\text{i}+1}\) The point is used as the output data of the model; Input t\({}_{\text{i}+1}\) refers to the track point of the t\({}_{\text{i}+1}\) time and the past three times (t\({}_{\text{i}}\), t\({}_{\text{i}-1}\), t\({}_{\text{i}-2}\)) as the input data of the model and the output t\({}_{\text{i}+1}\) means The track point of t\({}_{\text{i}+2}\) at the next moment is used as the output data of the model. The original sequence was constructed from 226 sets of ship position data from AIS information and de-noised by wavelet threshold. Owing to the strong continuity of the ship sequence, the sym8 wavelet basis function, with strong continuity and symmetry, was used for threshold de-noising. The number of noise reduction times was set to 3, the heuristic threshold function was used to select the threshold and the soft threshold function was used for wavelet de-noising. Figure 4 shows a noise sequence and Figure 5 is a comparison between original and de-noised sequences. As can be seen from Figure 5, the presence of noise causes a slight disturbance in the position signal. To verify the effectiveness of wavelet de-noising, we used ship position sequences with and without de-noising to predict ship trajectories and compared the results.