| Citation: | WANG X L，YIN H，HE M. Potential conflict prediction of mobile targets in airfield areas based on LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（6）：1850-1860 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0505
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In view of the problem of frequent conflicts in airfield areas, a method to predict the potential conflicts of mobile targets in airfield areas based on long short-term memory (LSTM) network was proposed. According to the complex network theory, aircraft and vehicles were taken as the research objects, and the network of mobile targets in the airfield area was established. The dynamic evolution model of the network was set, and the operation data was input to calculate multiple characteristic indicators of the network. In addition, the principal component analysis of the indicator time series was carried out to synthesize the potential conflict indicator. A LSTM network model was built by using the Keras framework, and the indicator time series were input into LSTM network for training and prediction and compared with other prediction methods. The actual operation data of Xi’an Xianyang Airport were used for experiments. The predicted values were compared with the real values. The mean square errors of the predicted results of each indicator were 1.608%, 13.126%, 0.072%, 0.004%, and 0.014%, respectively. The results show that the potential conflicts can be described from different perspectives by using characteristic indicators of the network after the network model of mobile targets in the airfield area is built. LSTM network can effectively predict the potential conflicts in the network of mobile targets in the airfield area, remind relevant personnel to prevent conflicts, and reduce the probability of conflicts.
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