Attitude Measurement Algorithm Based On Integrated BDS /MEMS

With the rapid development of navigation technology as the navigation applications, the requirement for the performance navigation systems is constantly improved. Therefore, the navigation system which can provide not only accurate location, speed but also attitude information has caught more and more attention. The traditional INS attitude measurement system can only ensure to offer high accuracy navigation information in a short time. Especially for the low-cost MEMS sensors, the attitude precision is very low after it works for a long time. Satellite attitude measurement system can maintain high accuracy after a long-time working. However, it also has its own drawbacks. It is susceptible to the external environment interference and unstable in dynamic application. Therefore, in order to fully combine the advantages of the inertial navigation system and the satellite attitude measurement system, a MEMS aided ambiguity resolution method is proposed to improve the performance of BDS and then the integration of MEMS and BDS attitude information is realized by using the Extended Kalman Filter(EKF) method to further improve the dynamic performance and the accuracy of attitude measurement in challenge environment.Firstly, to solve the problem of low precision of the MEMS, the error models of MEMS accelerometers and gyroscopes are designed to calibrated fixed error. In order to improve the accuracy and reliability of MEMS sensor, the information of gyroscope and accelerometer should be preprocessed after calibration. The random noise removed by using wavelet transforms. Therefore, this thesis focuses on removing random noise and fixed offset to improve the performance of MEMS sensor attitude determination.Secondly, if the quality of the BDS signal is poor, the success rate of ambiguity resolution will be very low. While in real application it is common that the quality of the signal degrade rapidly due to signal attenuation, multipath interference caused by obstacle blocking. The thesis presents a method of using the MEMS attitude information to assist and improve ambiguity resolution algorithm. The method can strength float solution quality and optimize the integer ambiguity search space. Effectively assisted with MEMS sensors, it also fundamentally improves the performance of classic satellite attitude determination algorithm.Finally, to make full use of the high precision of BDS and to improve the dynamic performance and precision of the system, we deeply study the fusion algorithm of BDS and the sensor data. Because the BDS can provide the attitude information, so the BDS attitude can be taken as the observation information to eliminate the MEMS gyroscope drift during long-time attitude measurement. Test results show that the fusion algorithm can guarantee the good performance of the attitude measurement system which can offers accurate and stable attitude information in complex environments.