Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network Scientific Reports
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- Later, Pang et al.39 proposed a new Conditional Generative Adversarial Network (CGAN) method for the weather-related aircraft trajectory prediction problem.
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- Pang et al.38 combined convolutional layers with LSTMs to predict trajectories based on convective weather conditions in an aircraft’s flight plan before takeoff.
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- The prediction results of Bi-LSTM have a certain deviation at the prediction point around 0–40, but as the prediction continues, the deviation is gradually narrowing.
- The comparison of relative errors in longitude prediction results.
This algorithm can explain continuous state-dependent flight mode transitions. Zhang et al.10 proposed a modified interacting multiple model (M-IMM) to predict the aircraft trajectory. This model based on the IMM and considering the influence of interaction on the mean error of residuals. Radar systems are currently used by ground monitoring stations to observe aircraft trajectories.
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- The comparison of relative errors in latitude prediction results.
- Section «Aircraft trajectory prediction framework» presents the aircraft trajectory prediction framework proposed in this article.
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Finally, aircraft trajectories are predicted based on estimated aircraft status, flight patterns, and inferred pilots’ intentions. Liu et al.3 proposed an intent based trajectory prediction (IBTP) algorithm. Secondly, pseudo-measurements are generated based on the spatial information in the expected waypoint (for example, location and speed) to smooth the predict-ed trajectory.
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- Den et al.27 proposed a flight trajectory recognition frame-work based on recursive neural networks and validated it by real-time predicting the air-craft’s expected arrival time.
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This figure showing the comparison between the prediction results based on Bi-LSTM and those of other algorithms. 15, it can be seen that the predicted results of Adaboost, MLP, and SVR still differ significantly from the actual trajectory. The prediction results of Bi-LSTM have a certain deviation at the prediction point around 0–40, but as the prediction continues, the deviation is gradually narrowing.
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Lin et al.8 proposed an unmanned aerial vehicle path prediction algorithm to estimate flight trajectories, which can provide early warning before a collision occurs. This study assumes an airspace around a disaster site that requires multiple air support. At present, study on trajectory prediction of aircraft have been done extensively by related scholars. In order to improve the prediction accuracy of flight trajectory, scholars have developed state estimation model, dynamic model and machine learning model. Aviator hacking software may be advertised on reviews, forums, and chats.
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Secondly, the data cleansing method is proposed for ADS-B raw data. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft. Tastambekov et al.22 are based entirely on historical radar trajectory data and do not use any physical or aeronautical parameters. After these radar data are preprocessed, the aircraft trajectory is predicted by local linear function regression.