| Citation: | WANG Ershen, SONG Yuanshang, TONG Gang, et al. Improved conflict detection model of low-altitude flight based on support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 8-14. doi: 10.13700/j.bh.1001-5965.2020.0533(in Chinese)
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In order to ensure the flight safety of general aviation aircraft in low-altitude airspace, an improved model of flight conflict detection based on support vector machine (SVM) is proposed. First, according to the physical form and flight status of the aircraft, a protection zone suitable for the general aircraft is established. Then, the improved ID3 decision tree algorithm is used to reduce the search space to a local method to select aircraft with potential flight conflicts, and choose the appropriate training set by random forest (RF) method. Finally, the tanh function is used to optimize the probability mapping of the easily saturated sigmoid function to the SVM classification results. Through simulation verification and contrastive analysis, the results show that the DBSACN algorithm based on density clustering is used to remove outliers, and the data generated by false alarms and missing alarms are removed as the training set to optimize the SVM classifier. Therefore, using improved flight conflict detection model, the false alarms and missing alarms are reduced by 0.6% and 1.6% respectively, and the execution efficiency of the algorithm is improved. The model has better anti-interference ability and stability.
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