Abstract
Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.
Highlights
With the increasing popularity of location based service (LBS), as a key factor in LBS, the importance of positioning is widely acknowledged
Numerous scholars and groups are devoted to the research of indoor location, for example, indoor location technologies based on WIFI [1], RFID [2], Bluetooth [3,4,5], and wireless sensor networks [6]
As the above algorithms and methods are applied to pedestrian dead reckoning (PDR) navigation, experiments showed that the navigation error reduces 32.6% by axis-exchanged compensation and gait parameters analysis algorithm
Summary
With the increasing popularity of location based service (LBS), as a key factor in LBS, the importance of positioning is widely acknowledged. As for walking distance estimation, starting with the gait parameters of cadence and step length, [11] calibrates the step length model for each person with two hybridization filters, while it has the inconvenience of offline calibration To solve this problem, the authors proposed a real-time walking parameters estimation model in [12], where zeroapproximation step detection algorithm and walking speed are integrated into the above model to improve the accuracy. With the problems of deficiency and complexity in heading estimation, offline training, and inaccuracy in walking distance, to deal with the above problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm of PDR to achieve a relatively high accuracy indoor navigation by MEMS-IMU.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
