Study On Personal Navigation System Based On MEMS Inertial Devices

With the development of modern city as well as the encouragement and support of the 12 th five-year-plan scheme for the construction of smart city, indoor positioning service as an important part of the perception layer develops rapidly. With the deepening development of the Internet of things, people’s demand for the precise positioning service is increasing day by day.The SINS(Strapdown Inertial Navigation System) based on MEMS(Micro Electro Mechanical Systems) inertial devices is a fully autonomous navigation system, which own strong autonomy, high update rate, full navigation information, strong portability, and can realize the navigation completely independent.The issue on autonomous navigation is studied when pedestrian around the case of GPS signal is weak. Personal navigation systems based on MEMS inertial devices can be used to soldiers, police and security personnel who engaged in fire to provide a higher level of security; Also in underground surveying, jungle expeditions and other environment where GPS signals are weak or absent, PNS(personal navigation system)also play an important role in autonomous navigation.A new ZUPT(zero velocity update) algorithm for the shoe-mounted PNS based on low-precision IMU(inertial measurement unit) has been studied. Based on the cascade framework of Kalman filtering and particle filtering, the observability analysis on the state variables of Kalman filtering in the bottom layer is conducted,and the error of heading angle and position with bad observability in traditional Kalman filtering is thus removed; For the upper particle filter, the length of each step and the change of heading angle have been taken as the observed quantity, while the coordinates of heading angle and horizontal position have been taken as the state variables, to build the dead-reckoning motion model that integrates with indoor map information. The ZUPT algorithm with a mutual modification between Kalman filtering and particle filtering has been designed. Finally, the effectiveness and reliability of the new algorithm have been verified by using the indoor walking data of low-precision IMU.We conduct an experiment in the first level of the school jingguan Building. We thoroughly test and verify the newly proposed algorithm through several experiments under different speeds. Experimental results show that the system can control thepositioning error within 0.5%, the precision have a significant improvement over conventional localization algorithm.