# GAT-ABiGRU Based Prediction Model for AUV Trajectory
###### Abstract  
Autonomous underwater vehicles (AUVs) are critical components of current maritime operations. However, because of the complicated marine environment, AUVs are at significant risk of being lost, and such losses significantly impact the continuity and safety of aquatic activities. This article suggests a methodology for forecasting the trajectory of lost autonomous underwater vehicles (AUVs) based on GAT-ABiGRU. Firstly, the time-series data of the AUV are transformed into a graph structure to represent the dependencies between data points. Secondly, a graph attention network is utilized to capture the spatial features of the trajectory data, while an attention-based bidirectional gated recurrent unit network learns the temporal features of the trajectory data; finally, the predicted drift trajectory is obtained. The findings show that the GAT-ABiGRU model outperforms previous trajectory prediction models, is highly accurate and robust in drift trajectory prediction, and presents a new method for forecasting the trajectory of wrecked AUVs. AUV; trajectory prediction; GAT-ABiGRU 2024 14 4184 10.3390/app1401484 10.  
solves the problem of data isolation and ensures that the interactions between data features are fully considered. With GAT, the model can capture the spatial changes of the underwater environment more comprehensively, providing richer information for subsequent trajectory prediction. Secondly, we use a bidirectional gated recurrent unit network (ABiGRU) based on an attention mechanism to improve time-series data processing. This mechanism allows the model to focus on the most critical information by weighting and merging features, thereby improving prediction accuracy. Meanwhile, the bidirectional structure of ABiGRU enables it to make full use of the pre- and post-contextual information, further enhancing the sensitivity to future state changes. Finally, we fully account for the influence of marine environmental features on AUV trajectories. By taking these features as inputs, the model can better understand the relationship between ecological factors and AUV trajectories, further improving prediction accuracy. This innovative model design provides a new and effective underwater vehicle trajectory prediction tool. The contributions of this thesis can be summarized as follows:  
* A novel GAT-ABiGRU model is proposed to predict the trajectory of lost AUVs. This model utilizes a graph attention mechanism to extract spatial features from data and assigns different weights to the results. Simultaneously, the ABiGRU model incorporates an attention mechanism based on bidirectional gated recurrent units, allowing the model to respond more sensitively and integrate dynamic changes in marine environmental factors when processing time-series data. * The time-series data in this study are arranged into a graph structure, with each time step acting as a node and varying degrees of interaction between nodes. Using a graph neural network allows the model to prioritize nodes that significantly impact the prediction outcomes by dynamically adjusting the weights of neighboring nodes. * Various factors affecting AUV trajectory prediction, such as the kinematic information of historical AUVs and marine environmental conditions, were considered. Not only were the latitude and longitude of historical AUV data points taken into account, but ecological features, such as ocean current speed, temperature, and salinity at the AUV's disappearance location, were also considered to enhance the diversity of the data. The document's structure is as follows: The first part outlines the research background, the formulation of the problem, and the importance of the study. The second part reviews related work, summarizing research findings relevant to the research topic. The third part details the methodology, including the framework of the methods used in this study and the construction of the GAT-ABiGRU model.