SVM-BTS Based Trajectory Identification and Prediction Method for Civil Rotorcraft UAVs
Qingchun Jiao,
Lin Bao,
Huihui Bai
et al.
Abstract:To address the issue of low predictive accuracy in complex trajectory forecasting for civilian rotorcraft unmanned aerial vehicles (UAVs), this paper presents a method that utilizes the SVM-BTS technique for recognizing and predicting these intricate trajectories. Initially, the Support Vector Machine-Binary Tree Support Vector Machine model (SVM-BTS) is employed to segmentally recognize the complex trajectories of civilian UAVs. Based on this identification, five distinct flight states are identified: vertica… Show more
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