A Deep Learning Approach for Aircraft Trajectory Prediction in Terminal Airspace

Abstract

Current state-of-the-art trajectory methods do not perform well in the terminal airspace that surrounds an airport due to its complex airspace structure and the frequently changing flight postures of aircraft. Since an aircraft that takes off or lands in an airport must follow a specified procedure, this paper will learn a data-driven trajectory prediction model from many historical trajectories to improve the accuracy and robustness of trajectory prediction in the terminal airspace. A regularization method is utilized to reconstruct each aircraft trajectory to obtain a high-quality trajectory with equal time intervals and no noise. Furthermore, we formulate the 4D trajectory prediction problem as a sequence-to-sequence learning problem, and we propose a sequence-to-sequence deep long short-term memory network (SS-DLSTM) for trajectory prediction, which can effectively capture the long and short temporal dependencies and the repetitive nature among trajectories. The proposed model is composed of an encoding module and a decoding module, where the encoding mode realizes the feature representation of historical trajectories, while the decoding module accepts the output of the encoding module as its initial input and recursively outputs the predicted trajectory sequence. The proposed method is applied to a dataset for the terminal airspace in Guangzhou, China. The experimental results demonstrate that our approach has relatively high robustness and outperforms mainstream data-driven trajectory prediction methods in terms of accuracy.

Motion State Recognition and Trajectory Prediction of Hypersonic Glide Vehicle Based on Deep Learning

“…In order to verify the prediction effect of the proposed model, the following benchmark models are selected for comparison: ConvLSTM model (Chen et al, 2020) [33], SA-ConvLSTM model (Li et al, 2021) [34], SS-DLSTM model (Zeng et al, 2020) [35] and EMD_AA model (Li et al ,2021) [9]. ConvLSTM model is a widely used comparison method.…”

A GRU‐Based Method for Predicting Intention of Aerial Targets

“…To verify that the proposed characteristic prediction module was effective and efficient, it was compared with the RNN [ 38 ], LSTM [ 39 ], GRU [ 40 ], and BiLSTM networks in terms of both real time and mean square error, and a segment of the predicted trajectory with the characteristic of the distance between the two enemy sides was selected for comparison with the actual trajectory. The results are shown in Table 1 and Figure 10 .…”

Research on Method of Trajectory Prediction in Aircraft Flight Based on Aircraft Performance and Historical Track Data

“…For example, Zhao et al proposed a deep long short-term memory (D-LSTM) neural network for aircraft trajectory prediction, which improves the prediction accuracy of aircraft in complex flight environments [16]. Zeng et al [17] formulated the 4D trajectory prediction problem as a sequence-to-sequence learning problem and proposed a sequence-to-sequence deep long short-term memory network (SS-DLSTM) for trajectory prediction. Gabriel et al take turning points as clustering objects and use the clustering method to sort out the track data and exclude the departure track to extract the typical track, which is used for aircraft surveillance and prediction, but the track information loss is more, and there is a lack of altitude and time information [18].…”