论文标题
使用有监督的学习方法预测飞机飞机的机动状态
Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods
论文作者
论文摘要
航空车遵循基于纬度，经度和高度的引导方法。这些信息可用于计算沿轨迹线的机动车辆的机动状态。这是一个二进制分类问题，可以利用机器学习来解决此类问题。在本文中，我们提出了一种使用线性，距离度量，判别分析和增强集合监督学习方法来得出机动状态及其预测的方法。我们在结果部分中提供了各种指标，从而对适当的算法进行了缩短的比较，以预测操纵状态。
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.
