Multiple Model Kalman Filtering for MEMS-IMU/GPS Integrated Navigation
The conventional Kalman filtering algorithm requires the definition of a dynamic and stochastic model, and errors of low cost MEMS-IMU are likely to vary temporally. So the conventional Kalman filter exists limitation in MEMS-IMU/GPS integrated navigation. This paper presented the use of Multiple Model Adaptive Estimation(MMAE) where multiple Kalman filters were run in parallel using different dynamic or stochastic models in MEMS-IMU/GPS integrated navigation. And the modified multiple model Kalman filter was used in order to solve the limitation of Multiple Model Adaptive Estimation(MMAE). Using static tests, the algorithm designed was validated. The test results show that the modified multiple model Kalman filter can improve performance of MEMS-IMU/GPS integrated navigation system, compared to the conventional Kalman filtering algorithm. And using the designed algorithm, the positioning accuracy is better than 5m and velocity accuracy is better than 0.1m/ s2, and the attitude errors are less than 0.5 degrees on the static condition.
Kang-hua TANG Mei-ping WU Xiao-ping HU
National University of Defense Technology, China
国际会议
2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)
哈尔滨
英文
2007-05-23（万方平台首次上网日期，不代表论文的发表时间）