DarLoc: Deep learning and data-feature augmentation based robust magnetic indoor localization

Qinghu Wang, Jie Jia*, Yansha Deng, Jian Chen, Xingwei Wang, Min Huang, Abdol Hamid Aghvami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Magnetic-based indoor localization has attracted considerable attention due to the pervasiveness of geomagnetic fields and is free of extra infrastructures. However, existing approaches still face heterogeneity problems caused by the type of devices, the holding attitudes, and the moving speeds of pedestrians. To cope with it, we propose a novel deep learning and data-feature augmentation based magnetic localization framework (DarLoc). First, we invoke orientation-insensitive magnetic signal extraction to remove the Direct Current (DC) component of the sequence and to eliminate the impact caused by different holding attitudes and various mobile devices. Second, we propose novel data augmentation and feature augmentation methods to extract features of speed information, thus tackling the multi-scale sequence problem caused by different moving speeds. Finally, we propose a deep multi-scale spatial–temporal learning model to extract both spatial and temporal features of the augmented sequences and to provide a robust localization for people with various moving speeds and attitudes. Extensive experiments, including a total of 189 volunteers with 4 different mobile devices and multiple moving speeds, are employed to evaluate the performance of DarLoc over 14 months. Experiment results reveal that: (1) DarLoc obtains an average localization accuracy of 0.47 m and 0.56 m under constant and variable moving speeds scenarios, respectively; (2) DarLoc obtains about 41% and 60% localization accuracy improvement compared with the state-of-the-art localization methods.

Original languageEnglish
Article number122921
JournalExpert Systems with Applications
Volume244
Early online date14 Dec 2023
DOIs
Publication statusPublished - 15 Jun 2024

Keywords

  • Data and feature augmentation
  • Deep learning
  • Indoor localization
  • Multi-scale features

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