To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation.
This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.
The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time.
In order to improve the capability of situational awareness and operational efficiency by considering environmental impact, a prediction model for short-term flight emissions within en route airspace is proposed in this paper. First, the measurement method of fuel consumption and flight emissions based on actual meteorological data is established, and the pattern of flight emissions is analyzed. Then, an adaptive weighting approach is proposed by considering prediction results obtained from a long–short term memory (LSTM) prediction model and extreme gradient boosting (XGBoost) prediction model, respectively. Taking the Guangzhou area control centre (ACC) AR05 sector in central and southern China as an example, the model is trained and tested on emission datasets with three statistical scales, 60 min, 30 min, and 15 min. The result shows that the combined variable–weight prediction model has the greatest prediction effect compared to six other models. In terms of time scale, the prediction performance is best on the 60 min statistical scale dataset; larger statistical unit magnitudes of emissions during the predicting process show better short-term prediction performance. In addition, the increase in data features when training the model plays an essential role in promoting model accuracy. The model established in this paper has high prediction accuracy and stability, which is capable of providing short-term prediction of airspace flight emissions.
Synergetic trajectory planning of flights is one of the important goals of trajectory-based operation (TBO), and it is also a method to further improve the utilization of airspace resources with the increasing number of flights in recent years. In order to plan the four-dimensional trajectory (4DT) pretactically and comprehensively, match the flight traffic with airspace capacity, reduce congestion, potential conflicts, and fuel consumption thus improving the efficiency of flights, this paper conducts a method for synergetic trajectory planning in the en-route phase from the perspective of airlines. Firstly, the aircraft performance model, aircraft fuel consumption model, and atmospheric model are constructed according to the base of aircraft data (BADA3.11), and an airspace congestion prediction model is constructed based on the historical flow data of airspace. Secondly, a multi-objective synergetic trajectory planning model is established, and a solution method based on the non-dominated sorting genetic algorithm and simulated annealing algorithm (NSGA3-SA) for the problem of synergetic trajectory planning is designed. The simulation shows that the optimization model and the solution algorithm of NSGA3-SA can reduce fuel consumption by about 4.5% compared to the original flight plans and has a good effect on reducing congestion and avoiding conflicts. The running time of the NSGA3-SA can meet the operational requirements of the pre-tactical trajectory planning. The multi-objective optimization model and the solution algorithm proposed in this paper have great value for the research of flight plan optimization.
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