Optimization of Multisource Dynamic Model in TBO

Xia, Li; Yan, Hao; Zhang, Yichi

doi:10.1155/2022/3755574

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…There is a deep collaboration between all relevant parties to ensure that the entire flight cycle is "controlled and accessible". The literature published by the International Civil Aviation Organisation (ICAO) [3,4] also adopts the TBO concept using an engineering approach to improve four aspects: efficient flight paths, airport operations, systems and data, as well as optimal capacity. Trajectory prediction is an important technique for TBO [5,6], as accurate trajectory prediction directly affects the performance of aircraft flight management, traffic management, anomalous behaviour detection, and many other aspects.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism

et al. 2022

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Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extraction of trajectory features and difficulty in overcoming the short-term memory of time series in existing trajectory prediction, a trajectory prediction model based on a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) network combined with dual attention and genetic algorithm (GA) optimisation is proposed. First, to autonomously mine the data association between input features and trajectory features as well as highlight the influence of important features, an attention mechanism was added to a conventional CNN architecture to develop a feature attention module. An attention mechanism was introduced at the output of the BiLSTM network to form a temporal attention module to enhance the influence of important historical information, and GA was used to optimise the hyperparameters of the model to achieve the best performance. Finally, a multifaceted comparison with other typical time-series prediction models based on real flight data verifies that the prediction model based on hyperparameter optimisation and a dual attention mechanism has significant advantages in terms of prediction accuracy and applicability.

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Section: Introductionmentioning

confidence: 99%

Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism

et al. 2022

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Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

Ma,

Li,

Zheng

et al. 2024

Meas. Sci. Technol.

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With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network and bidirectional gated recurrent neural network. At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.

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Optimization of Multisource Dynamic Model in TBO

Cited by 2 publications

References 26 publications

Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism

Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism

Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

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