Aiming at improving the aircraft conflict detection precision in a single classifier, a probabilistic conflict detection algorithm based on ensemble learning was proposed. Firstly, aircraft conflict model was established for the purpose of selecting flight datasets. Secondly, the current positions, speed vectors, look-ahead time, estimated turning time and turning angles were extracted as features which were inputted to train the basic classifiers, and a meta-datasets were obtained. Afterwards, support vector machine is used as the second-level classifier, the meta-datasets was regarded as new features which is used to train the stacking meta classifier. Lastly, the conflict probability was solved by the Sigmoid function mapping method. Simulation results have shown that this algorithm has a high accuracy for conflict detection and is suitable for turning flight.
Application of ensemble learning algorithm in aircraft probabilistic conflict detection of free flight
01.05.2018
1491876 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A probabilistic approach to aircraft conflict detection
IEEE | 2000
|A Probabilistic Approach to Aircraft Conflict Detection
British Library Online Contents | 2000
|An ensemble approach for conflict detection in Free Flight by data mining
Online Contents | 2009
|