[time]
2020-08-25T10:20:20+02:00

[team_name]
Perceiving the World

[team_institution]
ZITN

[logolink]


[system_name]


[website]


[track]
4

[reference_person]
Xin Wang

[email]
wangxqd@163.com

[description]
Due to its advantages of autonomous navigation and free from environmental interference, IMU has been widely concerned and studied in the navigation field. 
The system focuses on the research of MEMSIMU's PDR algorithm, simulation and performance analysis, mainly including the following three aspects: 1. Initial alignment of MEMSIMU: Initial alignment plays a very important role in the navigation algorithm, and its effect will directly affect the accuracy of navigation parameters. Due to the limitation of the hardware performance of MEMS IMU, the alignment accuracy of the SINGLE MEMS IMU is poor, especially in the course Angle. Therefore, the system will study the information fusion method of magnetometer and MEMS IMU, so as to realize the initial alignment of MEMS IMU. 2. Pedestrian motion model: The motion state of IMU is a key problem in indoor pedestrian tracking algorithm, which determines the accurate use of observation information in extended Kalman filter. Therefore, the system will study the use of acceleration and angular velocity to establish a pedestrian motion model, to effectively judge the IMU motion state. When IMU is detected to be at rest, foot-mounted method and extended Kalman filter are used to accurately estimate and timely compensate the navigation parameter error and sensor error. 3. Indoor pedestrian flight path calculation algorithm: Based on THE MEMS IMU hardware platform and MATLAB simulation platform, the system adopts THE IMU strapdown algorithm to solve the navigation parameters, and simultaneously combines the pedestrian motion model, foot-mounted and extended Kalman filter to achieve error estimation and compensation, and then design the indoor pedestrian flight path calculation algorithm.

[references]