UAV Trajectory Prediction Based on Flight State Recognition
Jiandong Zhang, Zhuoyong Shi, Anli Zhang, Qiming Yang, Guoqing Shi, Yong Wu
Abstract
UAV trajectory prediction is the core technology for autonomous UAV flight and is a prerequisite for control and navigation. In this paper, the UAV flight path prediction model is established by collecting the flight data of the actual UAV. Firstly, the UAV flight information collection and data preprocessing are carried out; Secondly, the UAV flight state recognition model is established based on the PCA-SVM model to identify five UAV flight states; Finally, the flight path prediction model of UAV based on flight state recognition is established, and the neural network model is established based on the flight path of five flight state recognition. The experimental results show that: 1) The accuracy of UAV flight state recognition based on PCA-SVM is more than 90%. 2) The average prediction error of the traditional neural network UAV trajectory is 0.422m, and the maximum error of the circling state is 0.84m. 3) The average prediction error of the UAV flight path based on flight state recognition is 0.214m, and the maximum error of the circling state is 0.41m. The model error is less than 0.5m. The results show that the prediction model with flight state recognition has significantly less error than the direct UAV trajectory prediction, and the prediction model with flight state recognition predicts better than the traditional Unscented Kalman Filter method.