# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 2 Related Work
### Neural Network Model-Based Approach  
Unlike traditional dynamics modeling methods, neural networks have demonstrated significant advantages in adapting to complex environments, improving model generality, and optimizing prediction results. In particular, recurrent neural networks (RNNs), as a typical structure among neural networks, excel in accurately predicting future trends by analyzing time-series data. However, RNNs also face the challenge of vanishing or exploding gradients, which limits their application range and prediction accuracy to some extent [6, 7]. To overcome these challenges, researchers have actively sought innovations and developed a variety of improved RNN structures. Among them, the long short-term memory network (LSTM) and the gated recurrent unit (GRU) are two of the most representative improvement schemes [8, 9]. These improved RNN structures not only effectively alleviate the problem of vanishing or exploding gradients but also further enhance the stability and prediction accuracy of the model, providing more reliable and efficient tools to cope with the complex and changing real-world environment. LSTM improves the gradient problem by effectively replacing the hidden layer units in traditional RNNs through their unique memory units and gating mechanisms [10]. [PERSON] and other researchers [11] have optimized the LSTM and added a self-resetting feature that reduces the frequency of parameter computation and improves efficiency. As a simplified version of LSTM, GRU solves the overfitting problem with fewer parameters. It captures the long-term dependencies in the time series more efficiently due to its unique update-gate and reset-gate mechanisms. [PERSON] et al. [12] cleverly used a gated recurrent unit (GRU) model to process the time-series data, significantly improving the ship's trajectory prediction accuracy. Predicting the drift trajectory of a stranded AUV is a time-series prediction problem. At present, models such as recurrent neural networks (RNNs) [13, 14], long short-term memory networks (LSTMs) [15, 16], and transformers [17, 18] and their derivative versions have proven to be able to handle time-series prediction problems stably and have been widely used in research in recent years. [PERSON] [16] and [PERSON] [19] used the LSTM model for ship trajectory prediction, which promotes the rapid development of ship autonomous navigation technology. On the other hand, [PERSON] et al. [20] further enhanced the model's ability to capture the characteristics of time-series data by introducing a dual attention mechanism, thus significantly improving the accuracy and efficiency of trajectory prediction. [PERSON] et al. [21] proposed an innovative deep migration learning model that combines a bidirectional long short-term memory network (BiLSTM) and an attention mechanism for predicting the remaining useful life (RUL) of rolling bearings. This model improves the accuracy of RUL prediction and provides new ideas for research in related fields. In addition, [PERSON] and [PERSON] [22] proposed a CNN-LSTM model specifically for four-dimensional trajectory prediction of aircraft, which demonstrated higher accuracy and reliability in predicting the real-time position of aircraft (including latitude, longitude, altitude, and time) compared to traditional methods. The introduction of graph neural networks (GNN) marks significant progress in handling graph-structured data. Initially designed to address complex graph-related problems, such as classifying molecular structures, GNNs have found widespread applications in various fields, including recommendation systems, machine translation, and time-series analysis. Particularly in the field of data prediction, the use of models such as temporal graph convolutional networks (T-GCNs) [23] and graph attention temporal convolutional networks (GATCNs) [24] is becoming increasingly common.