Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System
摘要: 针对单一模型滤波器在未知或不确定的系统参数下适应性较差的问题,提出了一种新的基于多模型自适应估计(multiple model adaptive estimation, MMAE)的滤波方法.该方法利用改进的卡尔曼滤波代替传统的卡尔曼滤波,比如扩展卡尔曼滤波(extended Kalman filter, EKF)和无迹卡尔曼滤波(unscented Kalman filter, UKF).EKF和UKF 被用来作为多模型自适应估计的子滤波器,从而实现对非线性系统的状态估计.同时,还将该方法应用于基于弹道导弹模型的组合导航中实现了系统仿真.仿真结果表明,与传统的EKF和UKF算法比较,改进的滤波方法可以解决传统模型滤波器适应性差的问题,并提高系统的导航精度.Abstract: In this paper, a new filtering method based on multiple model adaptive estimation(MMAE) algorithm is proposed, for the problem of poor adaptability of single model filters with unknown or uncertain parameters. In this proposed algorithm, we use improved Kalman filters rather than traditional Kalman filters, such as extended Kalman filter (EKF), unscented Kalman filter (UKF). And EKF and UKF are used as sub filters in MMAE algorithm to realize the state estimation of nonlinear system. Meanwhile, this method is applied to the SINS/CNS integrated navigation system under the motion of ballistic missile. As the simulation result shows, the improved filtering methods have better navigation accuracy, and can solve the problem of poor adaptability of single model filter, when compared with traditional EKF and UKF algorithms.