Flight Conflict Detection Algorithm Based on Relevance Vector Machine

In response to the problems of slow running speed and high error rates of traditional flight conflict detection algorithms, in this paper, we propose a conflict detection algorithm based on the use of a relevance vector machine. A set of symmetrical historical flight data was used as the training se...

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Bibliographic Details
Published in:Symmetry (Basel) 2022-10, Vol.14 (10), p.1992
Main Authors: Wang, Senlin, Nie, Dangmin
Format: Article
Language:English
Subjects:
Accuracy
Aircraft accidents & safety
Algorithms
Aviation
Bayesian optimization
Classification
flight conflict detection
Flight safety
Game theory
Machine learning
Methods
Normal distribution
Probability
relevance vector machine
Resampling
Sparsity
Support vector machines
Training
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Summary:In response to the problems of slow running speed and high error rates of traditional flight conflict detection algorithms, in this paper, we propose a conflict detection algorithm based on the use of a relevance vector machine. A set of symmetrical historical flight data was used as the training set of the model, and we used the SMOTE resampling method to optimize the training set. We obtained relatively symmetrical training data and trained it with the relevance vector machine, improving the kernels through an intelligent algorithm. We tested this method with new symmetrical flight data. The improved algorithm greatly improved the running speed and was able to effectively reduce the missed alarm rate of in-flight conflict detection symmetrically, thus effectively ensuring flight safety.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14101992