# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 2 Related Studies
### Convolutional Neural Network  
Deep learning can mine the deep feature representation of the data and find the relationship between factors. Studies have indicated that the method of data representation is directly related to the effectiveness of training and learning. A good representation can eliminate the influence of irrelevant information and retain critical information for learning tasks, thus improving the processing ability of complex tasks [24]. As one of the basic model frameworks for deep learning, convolutional neural networks have a robust feature representation ability for image and has achieved great success in the field of image processing. Below is a brief introduction to CNN.  
The typical CNN model structure [28] is mainly composed of convolution, pooling, and full connection layers. To adapt the CNN model structure, the input data of CNN should satisfy the 2D structure [29]. Generally, the former parts of the networks are composed of the convolution layer and pooling layer, which are cross-stacked, wherein the convolution layer performs a convolution operation on the input by a plurality of convolution kernels to generate feature maps. The pooling layer, also known as the sub-sampling layer, reduces the dimensions of the feature map. Through convolution and pooling operations, the network extracts topological features hidden in the data step by step, generating high-level abstract features of the data. The latter parts of the network are fully connected layers, which perform reorganization and decoding of the extracted high-level features and output the results.  
The design ideas of CNN are sparse connection, weight sharing, and down-sampling strategy. [PERSON] et al. [28] points out that there is a local connection in the image space, and the convolution process is an extraction of local correlation features. Different from the fully connected network structure of multi-layer perceptron (MLP), the convolution layer establishes the local connection between the network layers, reducing the network scale and preserving the spatial structure relationship of the data, and conforming to the sparse response characteristics of biological neurons. Weight sharing means that different regions share weights, reducing network parameters further. The down-sampling strategy performs down-sampling on the feature map to reduce the feature dimension and optimize the network structure. Based on the above ideas, CNN can not only significantly reduce network parameters and avoid over-fitting, but also retain the topology of the input data.  
In this topic, the trajectory data of marine ships have local correlations in the space-time domain. The ship location change is related to historical track, and historical behavior affects the present as well as the future. Based on the history and current behavior characteristics of the moving target, the future status of the moving target can be predicted. The deep learning algorithm, especially CNN, performs excellently in deep feature representation, deep feature mining, and model prediction. CNN is widely applied in the image field, which takes advantage of the local correlation of target features in the spatial domain to realize feature association and feature learning. Because of the correction of the ship's movement state in the time domain, with inspiration from CNN's design ideas of local perception of connection and weight-sharing, this paper extends the local connection of the spatial domain into that of the temporal domain. The CNN network structure is used to extract the temporal and spatial features of the trajectory and achieve better prediction accuracy.