Model Aided MEMS Rotary INS Independent Navigation Technology For Vehicle

MEMS-INS/GPS is a widely used navigation and positioning system in civilian vehicles.However,GPS has multipath transmission errors,and its signals are easily blocked by buildings.Therefore,when the GPS signal is not available,vehicle needs to rely on autonomous navigation technology to provide navigation information.Although MEMS inertial devices have the advantages of high reliability,small size,low cost and low power consumption,when the GPS signal is not available,the error of MEMS inertial navigation system rapidly accumulates with time.Therefore,in recent years,vehicle autonomous navigation technology based on MEMS INS has gradually become one of the hot spots in the field of GPS denied navigation technology.In order to improve the vehicle autonomous navigation performance of MEMS INS,there are two approaches: one is to optimize the structure and algorithm of MEMS INS,and the other is to introduce new aided navigation system.In this paper,the MEMS rotary inertial navigation method and the vehicle model aided navigation method are analyzed.Firstly,rotation modulation(RM)technology is applied to INS usually requires high-precision rotation and control mechanism,but the cost and size of motor and control mechanism reduce the advantages of MEMS technology.Thus,combining with the characteristics of vehicles,the wheel is used as the rotation mechanism of the MEMS rotary INS to realize the rotation modulation.Based on this,a wheel-mounted MEMS INS rotation modulation method is designed.In this method,the position of the inertial device relative to the navigation coordinate is periodically changed by the rotation of wheel,thus,the influence of the inertial device error in one cycle on the navigation accuracy of the whole system can be compensated.The MEMS rotary INS of vehicle realizes effective combination of vehicle motion and rotation modulation technology.Secondly,considering the requirements and characteristics of the autonomous navigation,the vehicle model aided navigation method is studied from the perspective of information mining.The characteristics of vehicle motion are analyzed.The vehicle kinematics model is established by using the vehicle incomplete constraint condition and the motion equation.However,when the vehicle turns at middle or high speed,the lateral force of the vehicle is large,the characteristic can not be accurately described by the kinematic model,so the vehicle dynamic model is established from the view of rigid body dynamics.At last,the accuracy of motion information extracted from vehicle kinematics and dynamics model under different operating conditions are analyzed and compared.The feasibility of model aided inertial autonomous navigation technology is verified theoretically.Furthermore,in order to improve the accuracy of the vehicle model aided MEMS rotary INS,aiming at the different accuracy of the navigation parameters outputted by the vehicle kinematics and dynamics models under different driving conditions,an interactive multiple model(IMM)integrated navigation algorithm based on fuzzy rules is designed.In the different operating conditions,different vehicle models are selected to aid MEMS rotary INS,thereby improving the accuracy of integrated navigation system.The feasibility and validity of the algorithm are verified through simulation.Eventually,a simulation platform is established using Carsim/Simulink in order to verify the model aided MEMS rotary INS independent navigation method in this paper.The platform can realize the simulation of track generator,vehicle MEMS rotary INS,vehicle kinematics and dynamics model,vehicle sensors and integrated navigation system.What's more,Matlab GUI software is used to design and set the user interface,in order to carry out the simulation and visual display.The rotary inertial navigation method and the vehicle model aided navigation method are studied in this paper.The accuracy and reliability are improved from the view of new structure design and new information mining.The conclusions supply good reference for the improvement of the autonomous navigation.