Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Zeng, Weili; Chu, Xiao; Xu, Zhengfeng; Liu, Yan; Quan, Zhibin

doi:10.3390/aerospace9020091

Cited by 46 publications

(22 citation statements)

References 71 publications

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“…Aircraft 4D trajectory prediction is the basis for air traffic management techniques such as conflict detection, aircraft approach sequencing, and abnormal behavior monitoring [9]. UAV is a new type of aircraft without onboard pilots.…”

Section: Related Workmentioning

confidence: 99%

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Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

et al. 2022

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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.

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Section: Related Workmentioning

confidence: 99%

“…Previously, many studies have been conducted for the prediction of 4D trajectories, and there are three general models for the prediction of 4D trajectories: state estimation models, kinetics models, and machine learning models [9].…”

Section: Related Workmentioning

confidence: 99%

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

et al. 2022

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“…and then modifies the parameters ω i k of the maneuver model according to the model probability. Finally, the predicted flight state of each model at time t p is calculated through (8), in which the one with the maximum model probability used as the predicted intercept point for midcourse guidance. The detailed design process of the trajectory prediction problem is as follows.…”

Section: The Equations Of Midcourse Guidance Problem In Nsgv Intercep...mentioning

confidence: 99%

“…Reference [8] classifies trajectory prediction into three types: the state estimation model, the kinetic model, and the machine-learning model, and then the definition and basic process of trajectory prediction are introduced for the four-dimensional trajectory prediction of aircraft. But, due to the complexity of the maneuver methods of near-space-gliding aircrafts and the fact that they cannot be represented as a single maneuver model, the interacting multiple model (IMM) containing multiple maneuver models is widely used for trajectory tracking of such targets [9].…”

Section: Introductionmentioning

confidence: 99%

A Midcourse Guidance Method Combined with Trajectory Prediction for Antinear-Space-Gliding Vehicles

Wan

Zhang

et al. 2022

International Journal of Aerospace Engineering

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Near-space-gliding vehicles have variable maneuver modes and dramatic changes in their ballistic parameters, which lead to a need to accurately predict an intercept point based on predictions of their trajectories. First, a trajectory prediction method builds a set of time-varying maneuver models based on flight missions combined with an adaptive grid to infer maneuver modes. An interactive multiple-model method of variable structure is proposed to identify the characteristics of the maneuver mode by introducing a fading factor and the modified Markov probability transfer matrix and then predict the trajectory through numerical integration. In the midcourse guidance method, the prediction of the target trajectory is introduced, and the zero-control interception manifold with intersection angle constraints is designed as the midcourse guidance terminal constraint. For the calculation of the starting time of the boost phase, the optimization solution satisfying the remaining flight time constraint is realized based on the Newton-Raphson iterative method. The analytical expression of a guidance command based on zero-error-miss/zero-error-velocity is established on the basis of the optimal control theory to provide an optimal flight path guiding an NSGV fly toward a point of interception. The simulation results show that the trajectory-prediction method has high prediction accuracy and strong convergency for typical maneuver modes, and the proposed midcourse guidance algorithm meets the requirements of the zero-effort intercept manifold with the intersection angle constraints, which is of important theoretical significance and acts as a reference value for intercepting high-velocity maneuver targets.

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“…It cannot only be used for conflict detection between aircraft, but also a key component of flow prediction, and congestion prediction in the airspace. Trajectory prediction methods for civil aviation flights can be classified into three main categories [26]: based on the dynamic model, machine learning model, and flight plan. Deep learning methods have achieved good results in the field of midto-long-term prediction trajectory recently, and the dynamic usually has good performance in the short term of trajectory prediction.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

et al. 2022

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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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Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Cited by 46 publications

References 71 publications

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

A Midcourse Guidance Method Combined with Trajectory Prediction for Antinear-Space-Gliding Vehicles

Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

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