Deep Learning-Based PDR Scheme That Fuses Smartphone Sensors and GPS Location Changes
- Authors
- Kim, Kwan-Soo; Shin, Yoan
- Issue Date
- Nov-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Location awareness; Global Positioning System; Accelerometers; Gyroscopes; Magnetometers; Legged locomotion; Deep learning; Smartphone; in-and-outdoor localization; pedestrian dead reckoning; deep learning; supervised learning; inertial measurement unit; global positioning system
- Citation
- IEEE ACCESS, v.9, pp.158616 - 158631
- Journal Title
- IEEE ACCESS
- Volume
- 9
- Start Page
- 158616
- End Page
- 158631
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/41983
- DOI
- 10.1109/ACCESS.2021.3130605
- ISSN
- 2169-3536
- Abstract
- Pedestrian dead reckoning (PDR), a sensor-based localization method using a smartphone, combines multi-sensor data from an inertial measurement unit (IMU) generated by the movement of pedestrians and calculates the amount of movement change from a previous location using fusion of sensor data. In this study, we propose a method to improve the efficiency of a deep learning (DL)-based PDR scheme to solve problems associated with the existing PDR method. The proposed DL-PDR scheme solves the movement change of smartphone users as a regression problem by combining IMU and global positioning system (GPS) data. In this paper, we (1) describe the existing PDR methods and problems, describe the proposed DL-PDR scheme and the data collection process of the input sensor data and output GPS used for deep learning, (2) correlate the collected I/O data and conduct preprocessing to make the data suitable for learning, (3) apply data refining and data augmentation methods to provide efficient learning and prevent overfitting, and (4) Verify the performance of the proposed scheme. The localization performance between the proposed scheme and existing methods is compared in various buildings where continuous localization is possible owing to connected indoor/outdoor spaces.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Information Technology > ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.