Research On Beidou Attitude Measurement System Tightly Coupled With Inertial Navigation

The Bei Dou Navigation Satellite System(BDS)based attitude measurement technology is widely used in the fields of autonomous driving,unmanned aerial vehicles,ships,etc.These fields have high requirements for carrier attitude measurement accuracy,and it is difficult for a single sensor to meet the needs of low cost and high accuracy,and the combination of multiple attitude measurement methods is The most reliable method at present is the combination of multiple posture measurement methods.However,the combined attitude measurement system has low accuracy of the low-cost inertial devices themselves,and the combined attitude measurement system is susceptible to system disturbance and observation sloppiness.To address these problems,this thesis focuses on the gyroscope denoising algorithm and combined attitude fusion filtering algorithm,the main research content of this thesis is as follows:(1)The gyroscope error compensation method is studied,and an improved wavelet combined with ARMA model is designed to address the problem that the gyroscope white noise is difficult to be removed in the process of gyroscope denoising using the Auto Regressive Moving Average Model(ARMA)combined with Kalman filter.The method firstly improves the wavelet threshold solution by using the number of samples collected and the number of wavelet decomposition layers,and then uses the improved wavelet algorithm to perform wavelet denoising on the original gyroscope data;then uses the ARMA model to model the gyroscope random error;finally designs the Kalman filter algorithm to compensate the error of the modelled data.Experimentally verified,the method improves the problem that the white noise of gyroscope output data is difficult to be removed in the process of using ARMA model combined with Kalman filter denoising only,on the basis of which the gyroscope output angular rate RMSE accuracy is improved by about 3 times,and the gyroscope output error is effectively reduced.(2)A compact combination attitude measurement model was constructed,and aiming at the problem that the general Kalman filter in the attitude measurement model is easy to be affected by system disturbance and observation gross error,the attitude accuracy after filtering is not high,an robust adaptive Kalman filter algorithm based on baseline constraints was designed.This algorithm uses the length of the baseline vector measured by the carrier phase double difference equation to evaluate the quality of the measured value,and changes the application strategy of the adaptive factor in the filtering algorithm through the evaluated quality,so as to reduce the deviation caused by the use of the adaptive factor in the filtering process when the measured value is abnormal.Static simulation results show that compared with Kalman filtering,the proposed algorithm improves the accuracy of heading Angle and pitch Angle by 6.19%and 2.63%,respectively,when there is system disturbance error.When the measured value is abnormal,the accuracy of course Angle and pitch Angle is increased by 19.73%and 20.36%,respectively,with obvious improvement effect.(3)The designed algorithm is further validated by building a dual-antenna attitude measurement platform to collect dynamic data for simulation and analysis.It is verified that the attitude angle accuracy of the dynamic combined attitude measurement is significantly higher than that of the BDS dual-antenna measurement.Compared with the Kalman filter,adaptive Kalman filter and robust adaptive Kalman filter,the designed algorithm not only reduces the influence of observation coarseness,but also reduces the error caused by the improper use of the adaptive factor,and improves the accuracy and stability of the filter.Finally,a visual display interface is designed through Qt to display the carrier attitude angle change curve in real time.