一种提升单探测器复合跟踪中粗跟瞄子系统预测精度和稳定性的方法

Method for Improving Prediction Accuracy and Stability in Coarse-Aiming Subsystem of Single-Detector Composite Tracking

摘要:

针对单探测器复合轴粗跟瞄预测过程中数据利用率不高的问题,提出了基于卡尔曼滤波和最小二乘法的实时预测方法,并通过实测振动数据进行仿真验证。结果显示:随着步长的增加,数据点之间的关联性降低,导致预测准确性和稳定性下降；数据利用率的提高又会增加数据点关联性,提升预测准确性和稳定性。在预测步长为25ms即数据利用率为75%的情况下,达到预测误差最小和稳定性最优的平衡点。

Abstract:

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%.