Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods
This work addresses a domain-specific problem for aerial vehicle monitoring, but it is incremental as it applies existing supervised learning methods without introducing new techniques.
The paper tackled the problem of predicting maneuvering status for aerial vehicles as a binary classification task using latitude, longitude, and altitude data, and it compared Linear, Distance Metric, Discriminant Analysis, and Boosting Ensemble methods, providing various metrics to identify the best algorithm.
Aerial Vehicles follow a guided approach based on Latitude, Longitude and Altitude. This information can be used for calculating the status of maneuvering for the aerial vehicles along the line of trajectory. This is a binary classification problem and Machine Learning can be leveraged for solving such problem. In this paper we present a methodology for deriving maneuvering status and its prediction using Linear, Distance Metric, Discriminant Analysis and Boosting Ensemble supervised learning methods. We provide various metrics along the line in the results section that give condensed comparison of the appropriate algorithm for prediction of the maneuvering status.