Multi-model Vehicle Integrated Navigation Algorithm Based on Variational Bayesian Method
In order to solve the problem of the accuracy declination of general state estimation in the existence of outliers in GNSS measurement noise which has the thick tail characteristics caused by strong maneuvering driving,SINS/GNSS integrated navigation information fusion method is proposed based on variational Bayesian method.Firstly,a model of vehicle integrated navigation system is established.Abnormal measurement noise is modeled as the Student's t distribution.The system state and hidden variables are given by variational Bayesian method.Therefore,posterior estimation value of the model parameters can be obtained.Then,the dynamic interactive fusion of SINS/GNSS and SINS/OD subsystems is implemented by using the interactive multi-model to handle the problem of invalid measurement of GNSS in urban driving.Finally,the offline car experimental results show that the proposed method can effectively reduce GNSS measurement outlier noise sharply exerted on SINS/GNSS/OD integrated navigation system.It has much higher accuracy and robustness compared with the traditional interactive multi-model.
vehicle integrated navigationvariational Bayesian methodStudent's t distributioninteractive multi-modeloutlier noise
万方数据
维普
