IMU Indoor Pedestrian Dead Reckoning Research Based On Foot-mounted

With the rapid development of modern science and technology, people has the grate requires of positioning and navigation increasingly, especially in indoor environment, such as market、library、parking area and mine, which usually needs to realize the position information of users or devices rapidly and accurately. However, as radio propagation is affected by fade、multi-path and disturb in complex indoor, it is difficult to ensure the accuracy and stability of collected signal characteristics. Therefore, the positioning technologies based on some wireless networks (such as WiFi and ZigBee) can’t currently guarantee the position precision steadily and rightly in all environment.Inertial Measure Unit (IMU) is autonomous and free from the outside influence, so that it attracts extensive attentions and intensive research in navigation fields. In this paper, the theme is IMU indoor Pedestrian Dead Reckoning (PDR). Main research is the theory、simulation and performance analysis of Micro-Electro-Mechanical Systems (MEMS) IMU indoor PDR algorithm, which is mainly focused on the following three aspects:1. MEMS IMU initial alignment:it has a great important place in navigation algorithm, which will directly affect the accuracy of navigation parameters. As the hardware performance limitation of MEMS IMU, it has low alignment precision with MEMS IMU alone, especially in heading. So in this paper, a method is discussed based on information fusion of magnetometer sensor and MEMS IMU, and completes MEMS IMU initial alignment;2. Pedestrian motion model:IMU stance is the key issues in indoor PDR algorithm, which determines the using of observation accurately in Extended Kalman Filter (EKF). Therefore, pedestrian motion model is presented with accelerometers and gyroscopes for IMU stance detection effectively in this paper. It estimates the errors of navigation parameters and sensors exactly and compensates instantly based on foot-mounted and EKF when detected IMU is static;3. Indoor PDR algorithm:this dissertation proposes an indoor PDR method based on MEMS IMU hardware platform and MATLAB simulation software, which calculates the navigation parameters using IMU mechanization algorithm, and achieves the error estimation and compensate with pedestrian motion model、 foot-mounted and EKE The results demonstrate with many tests and simulations that the positioning error ranges from0.5%to2.0%of the total traveled distance, which accomplish highly precession in indoor PDR.