Abstract
The application of the Micro Electro-mechanical System (MEMS) inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of MEMS sensors, the redundant design is an effective method under the restriction of current technology. The redundant data processing is the most important part in the MEMS redundant inertial navigation system, which includes the processing of abnormal data and the fusion estimation of redundant data. A developed quality index of the MEMS gyro measurement data is designed by the parity vector and the covariance matrix of the distributed Kalman filtering. The weight coefficients of gyros are calculated according to this index. The fault-tolerant fusion estimation of the redundant data is realized through the framework of the distributed Kalman filtering. Simulation experiments are conducted to test the performance of the new method with different types of anomalies.
Highlights
The micro electro-mechanical system inertial measurement unit (MEMS IMU) has the advantages of being low cost, small volume and light weight, but the low precision and poor stability are still problems that cannot be ignored
Only a few Micro Electro-mechanical System (MEMS) gyros can reach the level of the tactical grade [1], and these tactical grade gyros are too expensive for common application
The experiments demonstrate that the proposed method can improve the accuracy and stability of MEMS IMU
Summary
The micro electro-mechanical system inertial measurement unit (MEMS IMU) has the advantages of being low cost, small volume and light weight, but the low precision and poor stability are still problems that cannot be ignored. In order to solve the problems above, this paper designs a fault-tolerant fusion algorithm for the redundant MEMS gyro systems. The measurement data quality index is designed as the evidence of the weighted fusion estimation algorithm. Based on the structure of the distributed Kalman filtering, a fault and outlier tolerant fusion method is presented. In this method, the outliers and faults are processed simultaneously to avoid the first two problems mentioned above.
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