基于GA-BPNN的自适应抗野值无迹Kalman滤波方法

为了满足飞行试验轨迹的试验测量需求,构建了一种高性能飞行试验目标轨迹测量系统.在此基础上,针对实际飞行测量中遇见的异常数据处理问题,提出了一种基于多机器学习改进的自适应Kalman滤波方法.该方法以传统的无迹Kalman滤波器(UKF)为基础,首先,通过引入采用遗传算法改进后的BP神经网络(GA-BPNN)来改进UKF算法,以实现对UKF全局误差的调控和修正,从而改善UKF的估计精度;然后,应用抗野值技术来充分剔除测量中的孤立型和斑点型异常点,实现对GA-BPNN-UKF的再次改进,有效提高滤波的鲁棒性;最后,应用仿真来验证提出新算法的有效性,并通过对实际飞行测量数据(通过建立的轨迹测量系统获得的实际数据)的实验分析来显示算法在实际应用中的有效性.

An Adaptive Anti-Outlier Unscented Kalman Filtering Method Based on GA-BPNN

In order to meet the needs of flight test trajectory measurement, a high performance flight test target trajectory measurement system is constructed. On the basis of this, an improved adaptive Kalman filtering method is proposed based on the multi machine learning to deal with the abnormal data in actual flight measurement. This method is to take the traditional unscented Kalman filter (UKF) as a basis. Firstly, BP neural network improved by genetic algorithm (GA BPNN) is introduced to improve the UKF algorithm, realizing the regulation and correction of UKF global error, and improving the estimation accuracy of UKF. Furthermore, the outlier resistant technology is used to eliminate the isolated and spotted outliers in measurement, realizing the GA BPNN The purpose of UKF''s further improvement, and improving the robustness of filtering. Finally, the simulation used to verify the effectiveness of the new algorithm and the experimental analysis of the actual flight measurement data (the actual data obtained through the established trajectory measurement system) shows that the algorithm is valid.