Key Technique Of Micro Inertial System Based On Redundant Gyroscopes

MEMS (Micro Electro Mechanical System) inertial sensors, with the advantages of low cost, small size, fine shock resistance, play an important role in the field of automobile industry, biomedical engineering, aerospace, mobile communications, national defense science and technology. However, comparing with conventional inertial sensors, the measurement accuracy of MEMS inertial sensors is so poor. Therefore, the technique of fault-tolerance with redundant sensors and information fusion can be adopted to improve its measurement accuracy and reliability, and to improve the performance of Micro inertial measurement system with navigation, positioning and control information. Relying on the going project in the laboratory, several aspects of Micro inertial attitude heading reference system (AHRS) are pre-researched, which lay the foundation for the further application.According to the characteristics of MEMS sensors and accuracy requirements of AHRS, this thesis puts the emphasis on the issues of AHRS overall design, redundant configuration, on-line fault diagnosis, laboratory and on-line error calibration of redundant gyroscopes.Considering the specifications of the MEMS AHRS, an overall scheme including MEMS inertial sensors, electronic compass and GPS is set forth. The technique of redundant configuration is brought into the MEMS gyroscope measurement unit. In the view of measurement accuracy and reliability, a new configuration with nine MEMS gyroscopes is proposed. Then its accuracy, reliability, and performance of reconstruction after failure are analyzed theoretically. The analysis results show that this redundant configuration can meet the design requirements.For the on-line fault diagnosis method of redundant gyroscopes, the performance of widely used method, optimal parity test (OPT), is analyzed by both theoretical and experimental aspects. Taking the limitation of the OPT into consideration, the support vector machine (SVM) is brought forward to the diagnosis method. Firstly, the effect of the SVM kernel function and parameters are studied in depth. Secondly, a simple and efficient parameter optimization algorithm is addressed. At last, a comparison test between OPT and SVM shows that the SVM method has higher recognition rate of failure, lower missing rate and false alarm rate.The exploratory study of laboratory calibration for redundant gyroscope system is carried out. By experiments and theoretical analysis, the redundant gyroscope error measurement model is improved continuously, and the arrangement of turntable test is designed. When designing the measurement equations of calibration filter, a method of zero-space amplification is proposed to improve the observability of filter states. Through observability analysis and simulation test, it indicates that all the states in gyroscope error model can be estimated correctively, which proves the effectiveness of this calibration method.Since the constant drift and random drift of MEMS gyroscope are larger than traditional ones, it is necessary to study how to calibrate them online. When calibrating the constant drift, the integrated navigation theory is recommended. Based on the error model of inertial navigation system, the position and velocity information from GPS is chose as reference value, and then the constant drift of gyroscopes is estimated real time by Kalman filter. In order to improve the observability of states, the maneuver of aircraft is designed. The simulation test shows that the maximum relative estimation error is less than 7%, which can meet the usual needs of flying task. As for the online calibration of random drift, the Kalman filter is used again. Firstly, the model of random drift is established in accordance with autoregressive moving average (ARMA) model, then 3 kind of transformation from ARMA model to state space model is analyzed by experimental and theoretical aspects. The effective transaction is obtained which is the foundation of follow-up study. Secondly, the angular velocity model is established by the method of maneuver target tracking, which can describe all kinds of angular velocity in the aircraft maneuver. Thirdly, in accordance with the time-variable characteristic of the random error, the Kalman filter with time-variable noise estimator is adopted for data fusion between random drift and angular velocity. At last, an experiment is designed to evaluate the performance of the models and the filter. The experiment result shows that the mean and standard deviation of the calculated heading error decreased by 53% after compensating the random drift.