Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data

Abstract

The position prediction of marine moving targets based on historical trajectories is an important assistance procedure for marine reconnaissance and surveillance. Limited by satellite access period, space-based historic trajectory data have sparse and uneven intervals. However, most current time-series prediction methods require uniform time intervals. For non-uniform time series data, common processing methods first use the interpolation algorithm to fit historical data, and then carry out predictions based on equal interval data after the uniform sample. The disadvantage is that the accuracy of the interpolation data will limit the prediction accuracy. In addition, the time-series prediction methods represented by the grey model (GM) and autoregressive model (ARM) can only deal with equal-interval time prediction, in which it is hard to satisfy the prediction demand of non-equidistant time. Aiming at the limitations of most time series prediction methods and meeting the requirement of long-term variable duration prediction, a novel trajectory prediction method for sparse and non-uniform time series data based on deep neural networks is proposed. Firstly, to maximize the mining of the original data features, the moving behavior features are extracted from the raw historical track data by calculating the information of position, velocity, and position change for feature extension. Then, because of the temporal coherence of the track data, and inspired by the design idea of local correlation of the convolutional neural network (CNN), the CNN model is used to excavate the navigation rules to achieve position prediction. Finally, training of the network model is accomplished based on historical track samples. The experiments are carried out based on the space-borne automatic identification system (AIS) observation data. Experimental results illustrate that the method behaves better than other methods with the superiority of lower requirements for sampling, stronger adaptability to data characteristics, and higher forecasting accuracy for long-term prediction. When applied to the satellite search of marine moving targets, the track prediction has the potential to reduce the uncertainty of target location and guide satellite searching missions, thereby significantly improving the searching efficiency of targets.