CA-LSTM: An Improved LSTM Trajectory Prediction Method Based on Infrared UAV Target Detection

Abstract

In order to improve the UAV prevention and control capability in key areas and improve the rapid identification and trajectory prediction accuracy of the ground detection system in anti-UAV early warnings, an improved LSTM trajectory prediction network CA-LSTM (CNN-Attention-LSTM) based on attention enhancement and convolution fusion structure is proposed. Firstly, the native Yolov5 network is improved to enhance its detection ability for small targets of infrared UAVs, and the trajectory of UAVs in image space is constructed. Secondly, the LSTM network and convolutional neural network are integrated to improve the expression ability of the deep features of UAV trajectory data, and at the same time, the attention structure is designed to more comprehensively obtain time series context information, improve the influence on important time series features, and realize coarse–fine-grained feature fusion. Finally, tests were carried out on a homemade UAV infrared detection dataset. The experimental results show that the algorithm proposed in this paper can quickly and accurately identify infrared UAV targets and can achieve more accurate predictions of UAV flight trajectories, which are reduced by 9.43% and 23.81% in terms of MAPE and MSE indicators compared with the native LSTM network (the smaller the values of these evaluation indexes, the better the prediction results).