Indoor Localization Fusing WiFi with Smartphone Inertial Sensors Using LSTM Networks

Mingyang Zhang, Jie Jia, Jian Chen, Yansha Deng, Xingwei Wang, Abdol Hamid Aghvami

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window based displacement scheme is designed to generate a time-series based feature dataset. We further apply the long short-term memory (LSTM) network for data fusion and localization on this dataset by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

Original languageEnglish
Article number9381997
Pages (from-to)13608-13623
Number of pages16
JournalIEEE Internet of Things Journal
Volume8
Issue number17
DOIs
Publication statusPublished - 1 Sept 2021

Keywords

  • Dynamical systems
  • Estimation
  • Handheld computers
  • Indoor fusion localization
  • Kalman filters
  • Location awareness
  • long short-term memory networks
  • pedestrian dead-reckoning
  • Prediction algorithms
  • time-series
  • WiFi localization.
  • Wireless fidelity

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