自适应卡尔曼滤波方法在光电跟踪系统中的应用

Application of adaptive Kalman filtering method in photoelectric tracking system

在光电跟踪系统中,图像采集装置相对控制系统传感器滞后,会使脱靶量出现误差,将导致控制系统的跟踪精度降低。为了提高跟踪精度,提出了一种用于补偿跟踪脱靶量数据的自适应卡尔曼滤波方法。首先,通过CSM模型计算当前时间的状态预测矩阵和预测误差方差矩阵;再根据强跟踪滤波器,利用残差序列计算调节因子;然后,利用调节因子校正预测误差方差矩阵和机动频率;最后,使用校正后的参数更新预测的输出信息。仿真与实验结果表明:在高机动情况下,采用自适应卡尔曼滤波算法,跟踪误差的均方根误差RMS约为传统算法的0.21倍,最大跟踪误差和均方根误差都有显著减小。

In photoelectric tracking system, image acquisition device lags behind the control system sensor, which causes an error in miss distance, which will result in a lower tracking precision of the control system.In order to improve the tracking precision, an adaptive Kalman filtering algorithm for compensating tracking miss distance data is proposed.Firstly, the state prediction matrix and the prediction error variance matrix of current time are calculated by the CSM model.According to the strong tracking filter idea, calculate the adjustment factor throughresidual sequence.Then, the adjustment factor is used to correct the prediction error variance matrix and the maneuver frequency.Finally, the output information of prediction is updated by using the corrected parameters.The results of simulation and experiment show that under the high maneuvering condition, using the adaptive Kalman filtering algorithm, the root mean square error RMS of the tracking error is about 0.21 times that of the traditional algorithm, andthe maximum tracking error and root mean square error are significantly reduced.