Method for Improving Prediction Accuracy and Stability in Coarse-Aiming Subsystem of Single-Detector Composite Tracking
To address the problem of low data utilization efficiency in the coarse-aiming prediction of a single-detector composite axis,a real-time prediction method based on Kalman filtering and the least-squares method is proposed and validated via simulations using actual vibration data. The results show that as the step size increases,the correlation between data points decreases,thus reducing the prediction accuracy and stability.However,increasing data utilization enhances the correlation between data points,thereby improving the prediction accuracy and stability. An optimal balance between minimal prediction error and optimal stability is achieved at a prediction step length of 25ms,which corresponds to a data utilization rate of 75％.
万方数据
维普
