# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 2 Related Work
### Data-Driven Methods  
With the rapid development of computer technology, data-driven methods represented by machine learning and deep learning have been widely used in maritime data processing. Compared with the traditional mathematical modeling methods, the data-driven methods rely on the powerful arithmetic support of big data and artificial intelligence technology. Based on the artificial neural network model, the vessel trajectory restoration model is constructed to excavate the patterns and rules of the data itself, and the contextual information of the trajectory data features is extracted for trajectory restoration to achieve the comprehensive optimization and improvement of the AIS data.  
[PERSON] et al. [22] proposed an inland river trajectory complementation method based on a least squares support vector machine (LS-SVM) based on inland river vessel trajectories with spatio-temporal similarity characteristics, which seeks the most similar trajectories by adopting the quadratic matching method after synchronized processing of AIS trajectory data and uses the LS-SVM model to repair the mutilated trajectory data. [PERSON] et al. [23] built a joint model based on a back propagation (BP) neural network and segmented Hermite interpolation three times to repair and predict the trajectory data for the problem of AIS data loss and through comparison found that the joint model is more effective in repairing and predicting the vessel trajectory. [PERSON] et al. [24] proposed a trajectory reconstruction framework integrating data quality control and preprocessing to solve the problem of noise pollution in AIS data. First, they designed the data quality control framework to deal with abnormal trajectory data, and then they utilized an artificial neural network (ANN) to make the trajectory prediction, but this method does not apply to complex navigational environments. The neural network represented by recurrent neural networks can handle the time series data more effectively. [PERSON] et al. [25] improved on [PERSON]'s algorithm and proposed an inland river trajectory repair method based on bi-directional long short-termmemory recurrent neural networks (BLSTM-RNNs), which can make up for the lack of trajectory repair capability of the linear method and is suitable for inland river trajectory repair capability, which can be applied to vessel trajectory repair scenarios with complex geometries and multiple missing points.  
In existing research, for the processing of a large amount of sparse and abnormal trajectory data, the conventional method is to identify and remove abnormal data and then use neural network methods with RNN as a variant for missing trajectory restoration. However, these methods often neglect the potential value of abnormal trajectory points during processing and neglect the correlation between trajectory features, resulting in the inability to fully use the intrinsic information of the trajectory data and the simplicity of the model, thus affecting the effect of trajectory restoration to a certain extent. Therefore, it is necessary to use the correlation analysis method to construct a composite indicator of features correlation, accurately determine the model input features of the effective vessel trajectory, and then repair the vessel trajectory based on the Seq2 Seq network-constructed vessel trajectory restoration model to ensure that a more accurate and complete vessel trajectory is obtained.