@article{GUO2025, 
author = {Xingyu GUO and Jiaxin LI and Hua WANG and Junnan LIU and Yafei LI and Mingliang XU},
title = {Recognition of carrier-based aircraft flight deck operations based on dynamic graph},
year = {2025},
journal = {Chinese Journal of Aeronautics},
volume = {38},
number = {3},
keywords = {Carrier-based aircraft, Flight deck operation, Operation recognition, Long spatial–temporal trajectories, Dynamic graph, Biased random walk, Graph embeddings},
url = {https://www.sciopen.com/article/10.1016/j.cja.2024.09.032},
doi = {10.1016/j.cja.2024.09.032},
abstract = {Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial– temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.}
}