MEMS-IMU/GPS组合导航中的多模态Kalman滤波器设计

一般的Kalman滤波器要求有准确的动态和统计模型,而低成本的MEMS-IMU性能随着温度急剧变化,故在MEMS-IMU/GPS组合导航系统中使用一般的Kalman滤波器存在很多的局限性。针对低成本的MEMS-IMU/GPS组合导航系统,提出了多模态自适应滤波算法在MEMS-IMU/GPS组合导航系统中的应用;针对普通的多模态算法中的问题,采用修正的多模态自适应滤波算法来提高MEMS-IMU/GPS组合导航系统的性能。使用静态实时测试数据,验证了所提出的算法。测试结果表明,与普通Kalman滤波器相比,修正的多模态滤波算法提高了MEMS-IMU/GPS组合导航系统的性能;采用所提出的算法,MEMS-IMU/GPS组合导航系统的短时间静态位置精度小于5m(标准差),速度精度小于0.1m/s(标准差),姿态角精度小于0.5°(标准差)。

Multiple model Kalman filtering for MEMS-IMU/GPS integrated navigation

The paper presents a Multiple Model Adaptive Estimation in which multiple Kalman filters work in parallel using different dynamic or stochastic models in MEMS-IMU/GPS integrated navigation. The modified multiple model Kalman filter was used in order to solve the limitation of Multiple Model Adaptive Estimation. The proposed algorithm was validated by the static tests which show that the modified multiple model Kalman filter can improve performance of MEMS-IMU/GPS integrated navigation system. Finally,using the designed algorithm,the positioning accuracy is better than 5 m,the velocity accuracy is better than 0.1 m/s,and the attitude errors are less than 0.5 degrees under the static condition.