# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 5 Discussion  
The algorithm experiments are introduced in detail in Section 3. The experimental conditions, including the experimental dataset, experimental procedure, and evaluation metrics, are presented, and then, the experimental results based on the experimental dataset are given. Through the analysis of the variation of the loss function in the training process, it was verified that the model is well-fitting without overfitting or underfitting problems. Some of the prediction results and the prediction trajectory curve are also shown. Furthermore, comparison experiments of different algorithms were carried out. Based on the above experimental process, we present an extensive discussion of experimental results as follows. First, according to the prediction results presented in Table 2, overall, the longer the forecast, the greater the prediction error, which is consistent with our view that the longer the forecast, the greater the location uncertainty and the more complex the forecast. However, there were a few opposite cases, that is, in which the prediction error was instead more prominent for shorter prediction durations. Through the tracking of the prediction sample data, it was found that the larger errors mainly occurred in the ship steering stage, that is, when the prediction moment is in the ship turning phase, it increases the prediction difficulty, and thus, the prediction error is more prominent. In addition, the prediction errors are smaller when the ship is on a smooth sail. This can be seen in Figure 6. The method extracts extensive motion features from raw trajectory data. However, ship steering is a change in motion state that cannot be inferred simply from motion features, and the reasons for the sudden turning of ships are usually complicated. How to combine more factors, such as meteorological data and geographic data, to make predictions and thus improve prediction accuracy for complex scenarios such as steering, is a problem that, in order to be resolved, requires further research. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & \begin{tabular}{c} \(RTIME_{A}\) \\ **(Degree/h)** \\ \end{tabular} & \
\begin{tabular}{c} \(RTIME_{\varphi}\) \\ **(Degree/h)** \\ \end{tabular} &
\begin{tabular}{c} \(RTIME_{Dis}\) \\ **(km/h)** \\ \end{tabular} \\ \hline BP network with only position features [16] & 0.345 & 0.146 & 41.654 \\ BP network with multi-dimensional & 0.305 & 0.127 & 32.513 \\ trajectory features [16] & 0.189 & 0.161 & 28.777 \\ CNN with only position features & 0.157 & 0.118 & 20.610 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with other solutions. In addition, through the comparison experiments of different algorithms in Table 3, with the analysis of experimental results reported in related works, we can conclude that the comprehensive performance of neural network algorithms such as CNN and BP is better than that of statistical algorithms such as fitting extrapolation and autoregressive when applied to the track prediction problem. For the LSTM neural network, it is applicable to processing temporal series data because of its ability of long and short-term memory. The LSTM neural network with the design of time as input and position as output is adopted in the comparison experiment but does not realize better results. This may be related to the design architecture of the LSTM model and its training strategy. Simple strategies that consider trajectory as three-dimensional record sequences (time, latitude, longitude) may lead to the hard convergence of LSTM models for complex navigation patterns learning. The CNN network in this paper, on the other hand, achieves the purpose of temporal correlation learning through its local correlation and weight-sharing properties and obtains a better-fitting effect. According to Table 4, we analyzed the effects of different input features on prediction accuracy using both BP and CNN network structures.