摘要
传统卡尔曼滤波应用于捷联惯导初始对准中由于模型参数、噪声的统计特性不确定,影响估计效果。而模糊自适应卡尔曼滤波能按照模糊推理原理逐步校正系统的观测噪声协方差阵,具体实现是通过观察残差的理论值是否接近于其实际值,系统调整观测噪声协方差的加权以达到修正观测噪声协方差阵的目的,进而提高系统的对准效率。在噪声统计特性未知时,比较了常规卡尔曼滤波与模糊自适应卡尔曼滤波在初始对准中的应用效果。仿真结果表明,这种算法能有效提高系统的滤波效果,是一种较理想的初始对准滤波方法。
The application of conventional Kalman filtering to initial alignment of strapdown inertial navigation system（SINS） may influence the estimation effect due to the uncertainty of statistic characteristics of the model pa rameters and noise. The fuzzy adaptive Kalman filtering algorithm can modify the measurement noise covariance ma trix gradually through Fuzzy Inference System （FIS）. The detailed method is that the weighting of the observing noise covariance is adjusted by the system to correct the measurement noise covariance by observing whether or not the theoretic value of the residual closes to the real measurement covariance, and then the alignment efficiency of the navigation system can be improved. When the noise statistic characteristics are unknown,the initial alignment effects of the conventional Kalman filtering and the fuzzy adaptive Kalman filtering have been compared. The simulation re sults show that the proposed algorithm can improve the filtering performance of the navigation system effectively, and it is an ideal navigation filtering method of initial alignment.
出处
《压电与声光》
CSCD
北大核心
2013年第1期59-62,共4页
Piezoelectrics & Acoustooptics
