教師あり学習による航空機の機動状態予測
Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods
緯度・経度・高度の情報から航空機の機動状態を二値分類で予測する手法を提案し、線形・距離指標・判別分析・ブースティングなどの教師あり学習手法を比較評価した。
著者: Abhishek Gupta, Sarvesh Thustu, Riti Thakor, Saniya Patil, Raunak Joshi, Ronald Melvin Laban
分類: cs.RO, cs.LG
原文アブストラクト
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.
