A Novel Measurement Method Based On Multi-mode Information Perception Of Moving Vehicle Trajectory

In recent years,intelligent vehicles are developing rapidly,the traditional positioning methods and systems have been unable to provide high-precision real-time trajectory measurement.In China,the operation environment of the carrier is complex and changeable,which makes the traditional positioning mode appear single.With the continuous development of GNSS(Global Navigation Satellite System)in various fields and industries,and the gradual improvement of Bei-Dou satellite navigation independently developed in China,in the face of the actual operation needs of many industries,the positioning method of multi-mode information dominated by BDS(Bei-Dou Navigation Satellite System)has become an important research direction by more and more people.This thesis mainly studies the method of motion vehicle trajectory measurement based on multimode information perception,and studies the error analysis and suppression of inertial sensor under multimode information perception,the real-time correction method of dynamic model bias,and the data fusion method under multimode information perception.The specific research methods and innovations are:First.Error analysis and suppression of inertial sensor under multimode information sensing.By analyzing the error sources and types of the inertial measurement unit and describing the model of data output,an improved RLS(Recursive least squares)method for gyro noise reduction is proposed.On the one hand,the error estimation equation of the RLS method is modified to improve the processing of outliers,on the other hand,the adaptive parameter selection method is improved so that it can be adjusted according to the error changes in time series.Second.Research on real-time correction method of dynamic model deviation.A real-time correction method for dynamic bias is presented for dynamic models in EKF(Extended Kalman Filter).Consider using LSSVM(Least Squares Support Vector Machine)to train and predict the dynamic deviation of the model,and introducing the predicted result into the EKF process through the unscented transformation,combining the two methods to improve the accuracy of deviation correction and the accuracy of the dynamic model in practical applications.Third.Research on data fusion methods based on multimodal information perception.By explaining the basic theory of multidata fusion,this thesis presents a method of data fusion based on particle filter to improve UKF(Unscented Kalman Filter).Based on different spatial models,it can be used in the process of local and central fusion respectively.At the same time,a multimodal information-aware trajectory measurement system is designed,which combines BD/INS(Inertial Navigation System)/coded odometer,and is used with both platform and fusion methods.This improves the accuracy of trajectory determination and system continuity.The above three aspects mainly focus on the error of inertial sensor,the dynamic deviation of navigation positioning calculation model,the data fusion method in the process of multimode information trajectory determination,and the design of specific multi-mode information perception platform.The improvement of algorithms and methods in these aspects will help to improve the accuracy of internal sensors,and also improve the final track of the system.Accuracy of trace results.At the end of the thesis,simulation experiments,in-vehicle experiments and train data experiments are carried out through the self-developed multi-mode information perception system,which proves the validity and reliability of the methods proposed in this thesis,and provides some reference value for the research of the multi-mode information perception method of motion vehicle trajectory measurement.