R-T-S平滑算法与抗差Kalman在数据后处理中的应用

为了提高组合导航系统后处理精度和数据稳定性,将R-T-S最优固定区间平滑算法引入数据后处理中,在前向Kalman滤波的基础上,进行了后向R-T-S最优固定区间平滑处理,并针对GPS观测值中存在异常的问题,将抗差Kalman滤波算法引入数据后处理中,并对该算法进行实物仿真。结果表明,与传统Kalman滤波相比,R-T-S平滑算法不仅可以提高位置、姿态精度,而且在卫星信号失锁的情况下精度也得到显著改善,并且在不丢星的时刻,抗差Kalman滤波可以有效处理GPS信号中的异常观测值,遏制滤波发散,是一种有效的数据处理方法。

The Application of R-T-S Smoothing Algorithm and Robust Kalman Filtering in the Post-processing of the Integrated Navigation

In order to improve the post-processing accuracy of integrated navigation and the stability of data, the R-T-S (Rauch-Tung-Striebel) optimal fixed-interval smoothing is introduced into the post-processing of data. On the basis of the forward Kalman filter, the backward R-T-S optimal fixed-interval smoothing is added to the system, and the robust Kalman filtering is introduced into the post-processing of data to deal with the outlier in the progress as well. The measured data has been used to verify the algorithm. The results show that, compared with the traditional Kalman filter, the R-T-S optimal fixed-interval smoothing can not only improve the precision of the position and attitude, but can also improve the precision significantly in the case of lock-lose; besides, when GPS signal is available,the robust Kalman filtering can effectively deal with the outlier in the observation of GPS signals and restrain the filtering divergence, making it possible to be an effective way of data processing.