Machine Learning Driven Method for Indoor Positioning Using Inertial Measurement Uni

Jun Deng, Qiwei Xu, Aifeng Ren, Yupeng Duan, Adnan Zahid, Qammer H. Abbasi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The application of inertial measurement unit (IMU) is widespread in many domains, but the main hindrance in localization is the errors accumulation in the integration process over a long time. Recently, we notice that many researchers have applied machine learning (ML) algorithms to indoor positioning by using IMU sensor data, which sufficiently proves that the 6-dim data collected by IMU sensor contain a lot of information. In this paper, we present a ML driven method to make a regression between IMU sensor data and 2-D coordinates. To build a regression model with better generalization and lower computational complexity, this paper carries out feature extraction in the time-and time-frequency domain. The simulation run on Intel core i5-4200h shows that the method is able to suppress the drift of the inertial navigation system after a long-time travel. In comparison of GPS+IMU using extended Kalman filtering (EKF), the positioning RMS of our method on circular trajectories with a radius of 7 meters and 10.5 meters is reduced by at most 70.1% and 86.1%, respectively.
Original languageEnglish
Title of host publication2020 International Conference on UK-China Emerging Technologies (UCET)
PublisherIEEE
ISBN (Electronic)9781728194882
DOIs
Publication statusPublished - 29 Sep 2020
Event5th International Conference on the UK-China Emerging Technologies 2020 - Glasgow, United Kingdom
Duration: 20 Aug 202021 Aug 2020

Conference

Conference5th International Conference on the UK-China Emerging Technologies 2020
Abbreviated titleUCET 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period20/08/2021/08/20

Keywords

  • Feature extraction
  • Trajectory
  • Time-frequency analysis
  • Kalman filters
  • Navigation
  • Machine learning
  • Measurement units

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