## 4 Conclusions  
In this paper, a novel neural network model, AWOA-DBiLSTM, is proposed for tracking and predicting AUV trajectories. This study split the AUV dataset into training and testing sets in order to execute the experiments. The testing set was used to assess the model's performance after it was trained using the training set. The collected data were divided into features in order to increase the accuracy of the AUV trajectory prediction. DBiLSTM was then used to extract features from the divided position data and physical attitude data, and the gating mechanism was used to filter the data features to increase the accuracy of the prediction. In the meantime, the model proposed in this study extracted the features of the divided data separately, which increased the model's flexibility and made it easier to better fuse the data features. In addition, designing a reasonable neural network structure was a key step in constructing a high-performance, efficient, and interpretable model, and a well-designed neural network structure can enhance the model's interpretability in addition to maximizing its capacity for feature extraction. In this paper, to make the structure of the WOA-DBiLSTM neural network model more reasonable, we introduce the AWOA method to optimize the structure of the proposed model so as to improve the model performance. The experimental results demonstrate the outstanding prediction capabilities of the AWOA-DBiLSTM model established in this study for AUV trajectory prediction; the model predicted RMSE values for latitude, longitude, and depth of 0.00026, 0.00037, and 0.03992, respectively. In order to test the prediction effect of the AWOA-DBiLSTM model, the model was compared with the BiLSTM prediction model without feature differentiation and the DBiLSTM prediction model without adding the AWOA method. The experimental results show that the model proposed in this study shows better performance than other models in predicting AUV trajectories, where the model without feature differentiation has the worst prediction effect, followed by the DBiLSTM model without neural network structure optimization, and the best prediction performance is the AWOA-DBiLSTM model proposed in this paper. When compared to the DBiLSTM model, the RMSEs of AWOA-DBiLSTM for latitude, longitude, and depth predictions are improved by 0.00156, 0.00044, and 0.17242, respectively. In addition, in order to evaluate the generalization ability of the model, AUVs from different sea areas were put into the proposed model for prediction in this study and also achieved excellent prediction results. In this paper, a new neural network model is proposed, which achieves excellent prediction performance in AUV trajectory prediction. Despite the good prediction accuracy achieved in this study, there is still room for improvement. In future studies, in addition to considering the physical attitude and trajectory of the AUV, more influencing factors will be considered, such as changes in the underwater environment and the geographic location of each sea area where the AUV is located. This will help to improve the generalization of the model so that it can be applied to prediction studies in complex environments. Conceptualization, S.G. and J.Z. ; methodology, S.G.; software, S.G.; validation, S.G., J.Z., and T.Z. ; formal analysis, S.G.; investigation, S.G.; resources, S.G.; data curation, S.G.; writing--original draft preparation, S.G.; writing--review and editing, S.G., J.Z., and T.Z. ; visualization, S.G. and J.Z. ; supervision, J.Z. and T.Z. ; project administration, J.Z. and T.Z. ; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Lat** & **Lon** & **Depth** \\ \hline MSE & \(2.38\times 10^{-9}\) & \(8.56\times 10^{-9}\) & 0.01936 \\ RMSE & \(4.88\times 10^{-5}\) & \(9.25\times 10^{-5}\) & 0.13915 \\ MAE & \(4.69\times 10^{-5}\) & \(8.81\times 10^{-5}\) & 0.10997 \\ \hline \hline \end{tabular}
\end{table}