4D Trajectory Prediction of Aircraft Taxiing Based on Fitting Velocity Profile

Tang, Xin; Zhou, Long; Shen, Zhiyuan; Tang, Miao

doi:10.1061/9780784479292.001

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…Considering a DGP with only one hidden layer, the graphic model is depicted in Figure 2 [33]. The f x and f y are the nonlinear activation of a Gaussian process between layers, and the inference rules of the model are formulated as Equations (8) and 9:…”

Section: Deep Gaussian Processmentioning

confidence: 99%

“…(a) Kinematics and dynamics-based approaches (KDAs): this type of approach divides the flight operation into different phases based on its flight profile, typically climb, cruise and descent [7,8]. Considering the flight dynamics and aircraft performance constraints, several kinematics equations are built to illustrate the flight transition patterns for each phase [9], which are further solved to predict the flight trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…The x f and y f are the nonlinear activation of a Gaussian process between layers, and the inference rules of the model are formulated as Equations(8) and(9): Graphic model of a DGP with one hidden layer.…”

mentioning

confidence: 99%

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A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

2020

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In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller’s instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarning time.

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Section: Deep Gaussian Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

2020

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“…According to modeling approaches, the FTP methods can be classified into three distinct categories: physical models, filter-based models, and data-driven models. Physical models typically establish a set of mathematical equations based on kinematics and aerodynamics assumptions to estimate the future flight status (Tang et al 2015;Benavides et al 2014;FAA 2010), while the filter-based models estimate the flight trajectory iteratively via a predefined system model by considering realtime measurements (Kalman 1960;Yan et al 2013;Thipphavong et al 2013). However, limited by the fixed inference rules, these approaches easily suffer from error accumulation problems and not be suitable for multi-horizon FTP tasks.…”

Section: Introductionmentioning

confidence: 99%

FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework

Guo,

Zhang,

Yan

et al. 2024

AAAI

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Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers in managing airspace more safely and efficiently. Existing approaches generally perform multi-horizon FTP tasks in an autoregressive manner, thereby suffering from error accumulation and low-efficiency problems. In this paper, a novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) representation in the FlightBERT. Specifically, the FlightBERT++ is implemented by a generalized encoder-decoder architecture, in which the encoder learns the temporal-spatial patterns from historical observations and the decoder predicts the flight status for the future horizons. Compared with conventional architecture, an innovative horizon-aware contexts generator is dedicatedly designed to consider the prior horizon information, which further enables non-autoregressive multi-horizon prediction. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. The experimental results on a real-world dataset demonstrated that the FlightBERT++ outperformed the competitive baselines in both FTP performance and computational efficiency.

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“…e position, speed, height, and control sector of the flight passing through each reporting point can be obtained. For example, in [11], a method of predicting 4D taxiing trajectory for aircraft on airport surface based on fitting velocity profile is proposed. In [12], Schuster builds on an existing en route trajectory prediction (TP) model and develops novel techniques to predict aircraft trajectories for the transitions between the ground and en route phases of operation and the ground phase.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Luo

Liang³

2021

Mathematical Problems in Engineering

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Traditional 4D trajectory prediction based on aircraft performance models and flight procedures does not consider control handover rules. Meanwhile, method based on historical data mining cannot accurately couple with real-time conditions such as weather and also cause computational efficiency problems. This project collected a large amount of historical data to form a control experience database and mined the historical database to obtain control experience and flight intention. On the basis of the traditional aircraft performance model, this paper puts forward the aircraft maneuver mode using strategy and introduces the high-altitude wind information from the weather information into the aircraft 4D model to optimize the aircraft 4D trajectory calculation model. By comparing the flight forecast time with the real crossing time, it is found that the average error of the improved 4D forecast crossing time is less than 5% of the flight time, which is obviously better than that before optimization. It is proved that the optimized method based on historical track data is effective and reliable, and the accuracy of 4D track prediction is improved greatly.

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4D Trajectory Prediction of Aircraft Taxiing Based on Fitting Velocity Profile

Cited by 4 publications

References 2 publications

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework

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

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