Kim, K.S., and Shin, Y. (2021) Deep Learning-Based PDR Scheme That Fuses Smartphone Sensors and GPS Location Changes. IEEE Access, 9, 158616-158631.
https://doi.org/10.1109/ACCESS.2021.3130605

has been cited by the following article:

• TITLE: Personnel Localization of Real-Time Kinematic Based on Pedestrian Heading Projection Compensation in Substation Signal Interference Environment

  AUTHORS: Xiaolong Zhang, Tao Zhou, Jing Wang, Tao Wang, Qian Huang

  KEYWORDS: RTK, PDR, Complementary Filter, Step Size Estimation, Extended Kalman Filter

  JOURNAL NAME: Journal of Sensor Technology, Vol.13 No.2, June 30, 2023

  ABSTRACT: To address the intermittent positioning and drift of personnel positioning RTK in the high-frequency signal interference environment of substations, we propose to use IMU as the positioning compensation module of RTK and adopt the joint RTK/PDR positioning method to solve the positioning results. The heading angle is easily scattered in the pedestrian heading projection (PDR) process and the heading angles calculated from the output data of the gyroscope, accelerometer and magnetometer after denoising are input into the complementary filter for fusion. To improve the accuracy of step estimation in the PDR process, an improved step estimation model is used. For RTK/PDR data fusion, the extended Kalman filter (EKF) method is used, which helps to achieve outdoor full-scene high-accuracy positioning. The final simulation results show that RTK can be effectively compensated by PDR under the interference of high-frequency signals, and the positioning accuracy reaches 0.02 m.