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Similarly, Ma et al.37 combines CNN with LSTM to construct a deep learning hybrid model to predict the 4D trajectory of an aircraft. CNN is used to extract spatial features and LSTM is used to extract temporal features. Traditional trajectory predictors require multiple inputs, such as flight plans, aircraft performance models, weather forecasts. Many of these data are affected by environmental uncertainty. Based on Encoder-Decoder Architecture, Tran et al.41 build a deep learning model using GRU network without requiring aircraft performance and wind di-rection data.

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Aircraft ADS-B data sources

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  • 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.

De Leege et al.23 also use a stepwise regression algorithm to predict the arrival time of an aircraft based on historical data. Lin et al.24 also used historical flight track data as the source of raw data. Using the last flight plan before takeoff, Pang et al.25 proposed a probability aircraft trajectory prediction method based on Bayesian neural network. Wu et al.26 studied an aircraft 4-D trajectory prediction model based on BP network, using the airplane broadcast trajectory from Qingdao to Beijing as the data source.

Theoretical basis of neural network for predicting aircraft trajectories

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Data availability

At the same time, the relative prediction error of Decision Tree and Random Forest is also very small. The dataset of this article comes from the aircraft ADS-B data publicly provided by OpenSky. These volunteers receive ADS-B data from the aircraft and transmit the information to Opensky’s servers. The network transmission/data collection of Opensky is shown in Fig.

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The longitude, latitude, and altitude data of the aircraft are broadcasted through ADS-B. Abnormal data may be generated during transmission and reception, leading to a decrease in the prediction accuracy of the model. In this paper, the threshold method adopted to process abnormal data, the data within the threshold range is retained, and the data outside the threshold is eliminated.

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  • Then, the ADS-B data is com-pared with the predicted trajectory.
  • The point mass model (PMM) is commonly used in dynamic models.
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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.
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But LSTM solves the problem of gradient vanishing of RNN because LSTM introduces a gate mechanism. This mechanism is used to control the circulation and loss of features. Com-pared to the hidden state of the original RNN, LSTM adds a cell state. Tang et al.20 builds a hybrid system model to estimate the 4D trajectory of different types of aircraft based on their flight profiles and dynamic models under different flight conditions. Ren et al.12 proposes a framework for developing and validating trajectory modeling and prediction methods for various types of UAVs in standard and real-world environments.

Machine learning methods

The data in these tasks is data collected at different points in time, reflecting the changing state or degree of something, phenomenon, etc. over time. The general neural network is a fully connected network from the input layer to the hidden layer and then to the input layer. In the case of a CNN network, the data it processes is generally stand-alone, identically distributed (IID). These data are entered individually in a CNN network, and there is no relationship between the previous input and the latter.

  • De Leege et al.23 also use a stepwise regression algorithm to predict the arrival time of an aircraft based on historical data.
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If you play from a personal computer or smartphone, then access can be stored in the device’s memory. In other cases, when you log out of the account, make sure you have closed your profile.After entering the player gets to the main page of the site. LSTM can capture long-term dependencies in sequence data for time series estimation, but it only uses information from previous states46. However, sometimes predictions may need to be determined jointly by several inputs from the front and several inputs from the back, which will be more accurate.

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To improve the prediction accuracy, this article independently predicts the three-dimensional coordinates, and compares the errors with other prediction methods. This article simulates this situation using Opensky’s ADS-B data. Australia is the first country among the members of the International Civil Aviation Organization (ICAO) to announce the full adoption of ADS-B for airspace surveillance. The ADS-B information released one after another by the country’s air traffic control department points out that there is discontinuity in the ADS-B track. It can be seen that the FAA has also begun to worry about ADS-B data quality issues.

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Solves the problem that trajectory pre-diction performance is limited by the precision of predefined settings in existing databases. Kang et al.1 used visual sensors to obtain the position and attitude of the air-craft, and then deduced the conventional specific extreme power and an energy maintaining specific power. Finally, these powers were used in the particle dynamics model for trajectory prediction.

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