Tang Kanghua

Online estimation of DVL misalignment angle in SINS/DVL integrated navigation system

The misalignment angle between DVL and SINS is the key factor in determine the positioning accuracy of the SINS/DVL integrated navigation system. According to the problems appeared in practice, the error model of SINS was analyzed, and the DVL error model based on the misalignment angle online estimation was presented. The kalman filter technology is used to estimate the misalignment angle online in the integrated navigation process. The vessel test results show the variation of the stable estimation value is in the range of 0.1 degree, and the standard deviation is 0.067 degree, which meet the requirement. The navigation results attest the method can improve the positioning accuracy effectively, and prove the conclusion that the misalignment angle is a principal error source in the INS/DVL integrated navigation system.

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.