Research On Seamless Vehicle Integrated Navigation Technology Based On Inertial MEMS Devices

The seamless vehicle integrated navigation algorithm is the core technology of vehicle navigation in the satellite signal rejection area.Its integrated efficiency directly affects the overall positioning accuracy of the vehicular navigation system,especially when the satellite signal is weak such as vehicle storage,tunnel navigation,and building group navigation.These application scenarios are the signal rejection area.Because satellite signals are interfered in these application scenarios,the accuracy of satellite navigation and positioning is reduced.The satellite navigation system cannot provide continuous vehicle trajectories and precise positioning points.As a result,the vehicle trajectory is interrupted at the entrance of the garage or tunnel.It causes various positioning defects,such as integral deviation and track distortion.The vehicular navigation system that finally enters the satellite signal rejection area cannot achieve effective positioning information.It causes that the vehicular navigation fails and other serious problems.In view of the above-mentioned situation,this dissertation starts from the perspective of integrated navigation algorithm.focusing on the outdoor navigation,mode switching navigation,and indoor navigation in indoor and outdoor seamless navigation system.It is expected to explore a seamless indoor and outdoor navigation technology suitable for vehicles in a wide range of areas.It can carry out the model construction and algorithm analysis of the integrated navigation system respectively,and verify the validity of the proposed theoretical algorithm through on-board experiments.This dissertation focuses on the following four aspects:(1)Firstly,the dissertation models and analyzes the underground garage navigation in a typical seamless navigation environment.The integration of MEMS inertial system and GNSS system for outdoor navigation of vehicles is an effective scheme to meet vehicle positioning requirements and greatly reduce equipment costs.Indoor navigation system utilizes inertial navigation dead reckoning algorithm to meet the positioning needs in the unground garage in limited time.In this dissertation,an Extended Kalman Filtering(EKF)loose integrated navigation system is designed based on the principle of integrated navigation algorithm,and this is the framework of the seamless integrated navigation system.For processing the problem of unstable switching navigation,this dissertation summarized some experiences from the experiments.This dissertation utilized the innovative solution of multi-variable and multiple state switching,which has greatly improved the success rate of seamless vehicular navigation mode switching.The solution provides a stable and initial reference point for indoor navigation system.(2)Kalman filter integrated navigation system is only suitable for fully mechanized6-axis or above inertial navigation models,but not all 6-axis inertial sensors are fully utilized.There are many redundant sensors in IMU for vehicular navigation systems.In addition,if the inertial navigation system loses some dimensions,the Kalman integrated navigation system will be invalid.Therefore,this dissertation introduces a Reduced Inertial Sensor System(RISS).The dissertation also discusses the scheme to complete the indoor and outdoor seamless navigation system and the stability of the switching navigation system when the system only uses three-axis inertial sensors.In addition,the RISS system greatly simplifies the existing inertial navigation system.The RISS algorithm can also reduce the equipment cost of the seamless navigation system,and the commercialization of the system will be expected a better prospect.(3)Since the seamless navigation system only uses Dead Reckoning(DR)algorithms in the underground garage,it can only meet the precision needs of vehicular navigation in a short time.However,positioning errors of the inertial navigation system will be divergent in indoor DR navigation algorithm without calibration and the indoor positioning accuracy of the vehicle cannot be guaranteed.Therefore,this dissertation introduces the LIDAR SLAM algorithm after comprehensive evaluation to ensure the controllability of the indoor navigation error.Besides,the introduction of LIDAR sensors in indoor and outdoor mode switching algorithms provide more choices for predicting switching points,which can further improve the success rate of switching navigation algorithms.(4)In outdoor integrated navigation algorithms field,the existing Kalman filter integrated navigation algorithm and RISS integrated navigation algorithm can both meet the requirements of integrated navigation and positioning.It has become a difficult problem to quantitatively evaluate the pros and cons of the two integrated navigation algorithms.This dissertation summarizes the existing trajectory similarity algorithms and evaluates their applicable advantages,and proposes a vehicle trajectory evaluation standard.This standard algorithm is compared with the standard road network data,and finally a reliable navigation trajectory stability index is waited.