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.