Risley Prism Tracking Control Method Based on Social-STGCNN Trajectory Prediction
Risley prism boresight pointing control technology has been applied to flying target tracking in the military field.The coordinates of flying target time step directly affect the tracking accuracy of Risley prism.Therefore,carrying out flight target trajectory prediction will provide innovative planning and control ideas for Risley prism boresight pointing control.This article combines the Social-STGCNN deep learning method to optimize the Risley prism control algorithm.First,the data set is defined to obtain flight target information,and STGCNN is used to preprocess.Then the paraxial method is applied to obtain the Risley prism azimuth and pitch angle.Simulation analysis experiments are conducted.The results show that while achieving precise control of the Risley prism motor and the visual axis,an inter-axis error of 0.151 mm is obtained.This is 88.29%lower in the MAE index than the control algorithm that simply uses image error feedback,which effectively verifies the optimization effect and tracking performance of the control strategy proposed.
万方数据
维普
