Aircraft Trajectory Prediction Using Deep Long Short-Term Memory Networks

Zhao, Zili; Zeng, Weili; Quan, Zhibin; Chen, Mengfei; Yang, Zhao

doi:10.1061/9780784482292.012

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…Combined with the change in the ground plane motion state, an attenuation memory window is introduced to improve the hidden layer structure and further improve the prediction accuracy of the LSTM model. Zhao et al [72] proposed a deep long short-term memory (D-LSTM) neural network for aircraft trajectory prediction, which integrated the multi-dimensional features of aircraft trajectories into the LSTM to improve the prediction accuracy of aircraft in complex flight environments.…”

Section: Neural Networkmentioning

confidence: 99%

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Zeng

Chu

et al. 2022

Aerospace

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Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces the definition and basic process of trajectory prediction, including four modules: preparation, prediction, update, and output. In addition, the trajectory prediction methods are summarized into three types: the state estimation model, the Kinetic model, and the machine learning model, and in-depth analysis of various models is carried out. Further, the relevant databases required for the study are introduced, including the aircraft performance database, aircraft monitoring database, and meteorological database. Finally, challenges and future development directions of the current trajectory prediction problem are summarized.

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Section: Neural Networkmentioning

confidence: 99%

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Zeng

Chu

et al. 2022

Aerospace

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“…TCN layers encode a trajectory's spatio-temporal information into a latent vector without losing the underlying data's temporal (causal) relations [24]. We use TCNs as an alternative to using LSTMs [25] for encoding the trajectories.…”

Section: Implementation Detailsmentioning

confidence: 99%

Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

Jerry¹,

Scherer²,

Parv³

et al. 2022

Preprint

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Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies is a critical requirement to enable the real-world deployment of AI agents. Recently Signal Temporal Logic (STL) has been shown to be an effective language for encoding rules as spatio-temporal constraints. This work uses Monte Carlo Tree Search (MCTS) as a means of integrating STL specification into a vanilla LfD policy to improve constraint satisfaction. We propose augmenting the MCTS heuristic with STL robustness values to bias the tree search towards branches with higher constraint satisfaction. While the domain-independent method can be applied to integrate STL rules online into any pre-trained LfD algorithm, we choose goalconditioned Generative Adversarial Imitation Learning as the offline LfD policy. We apply the proposed method to the domain of planning trajectories for General Aviation aircraft around a non-towered airfield. Results using the simulator trained on real-world data showcase 60% improved performance over baseline LfD methods that do not use STL heuristics. [Code] 1 [Video] 2 *equal contribution This work was supported by Army Futures Command.

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“…In some respects, TP is a very amenable problem for machine learning due to the large quantity of radar observations available to train models [10,–13]. In recent years, methods based on neural networks [14,15] have been proposed for TP. However, there are several challenges facing machine learning methods for TP that must be addressed.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

et al. 2023

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Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant epistemic uncertainty, leading to discrepancies between observed trajectories and the predictions of deterministic models, hampering the task of planning to ensure safe separation. In this paper, a probabilistic model is presented, for the purpose of emulating the trajectories of aircraft in climb and bounding the uncertainty of the predicted trajectory. A monotonic, functional representation exploits the spatio-temporal correlations in the radar observations. Through the use of Gaussian process emulators, features that parameterize the climb are mapped directly to functional outputs, providing a fast approximation, while ensuring that the resulting trajectory is monotonic. The model was applied as a probabilistic digital twin for aircraft in climb and baselined against the base of aircraft data, a deterministic model widely used in industry. When applied to an unseen test dataset, the probabilistic model was found to provide a mean prediction that was 20.56% more accurate, as measured by the mean absolute error, with data-driven credible intervals that were9.54% sharper.

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Aircraft Trajectory Prediction Using Deep Long Short-Term Memory Networks

Cited by 17 publications

References 16 publications

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

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