SVM-BTS Based Trajectory Identification and Prediction Method for Civil Rotorcraft UAVs

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: vertical, pitch, transverse, roll, and transitional. To assess the predictive performance of these states, a combination of Sliding Window Polynomial Least Squares, Unscented Kalman Filtering, and Long Short-Term Memory neural network methods is utilized. Consequently, the most suitable prediction algorithm is determined for each flight state. Experimental results demonstrate that the SVM-BTS recognition method, compared to SVM, achieves a 10.4% increase in recognition accuracy. Across different flight datasets, this prediction method exhibits the lowest mean squared error values compared to SWPLS, UKF, and LSTM. Therefore, this study accurately predicts the complex flight trajectories of civilian rotorcraft UAVs, enhancing the precision and efficiency of UAV flight prediction.