Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects

Pang, Yutian; Liu, Yongming

doi:10.2514/6.2020-1853

Cited by 42 publications

(18 citation statements)

References 6 publications

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“…[107] developed a LSTM RNN for aircraft trajectory prediction. Similarly, generative adversarial networks (GAN) are also commonly used in aerospace prediction tasks with image-based input [108], [109]. GAN is an unsupervised generating learning model with a selfsupervised learning capacity.…”

Section: ) Data-driven Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

et al. 2022

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Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be a product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic stateof-the-art composition along with corresponding limitations, three dimensions of the future digital twins for aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

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Section: ) Data-driven Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

et al. 2022

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“…Such approach, i.e., trajectory clustering combined with multiple predictive models, were also employed for trajectory prediction in [20]- [23]. Other recent studies on the employment of different machine learning algorithms for trajectory prediction include Bayesian deep neural networks [24], [25], variational inference [26], conditional generative adversarial network [27], deep Gaussian process [28]. In addition, a hybrid machine learning-physics approach was recently proposed [29], in which an estimation algorithm (Residual-Mean Interacting Multiple Model) was introduced to improve a machine learning models by accounting for the motion of the aircraft.…”

Section: B Related Workmentioning

confidence: 99%

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

et al. 2022

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Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing lookahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

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“…Since neural networks (NNs) can approximate any continuous mapping well, it is a suitable improvement method compared to general linear regression. At present, more and more researchers use NNs to deal with trajectory prediction problems [39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Commonly used methods include Back Propagation (BP) NN, LSTM and Deep Neural Networks (DNNs), etc.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Zeng

Chu

et al. 2021

Aerospace

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Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.

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Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects

Cited by 42 publications

References 6 publications

Digital Twin in Aerospace Industry: A Gentle Introduction

Digital Twin in Aerospace Industry: A Gentle Introduction

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

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