Self-Calibrated Multi-Floor Localization Based on Wi-Fi Ranging/Crowdsourced Fingerprinting and Low-Cost Sensors

Qiao Wan, Xiaoqi Duan, Yue Yu, Ruizhi Chen, Liang Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Crowdsourced localization using geo-spatial big data has become an effective approach for constructing smart-city-based location services with the fast growing number of Internet of Things terminals. This paper presents a self-calibrated multi-floor indoor positioning framework using a combination of Wi-Fi ranging, crowdsourced fingerprinting and low-cost sensors (SM-WRFS). The localization parameters, such as heading and altitude biases, step-length scale factor, and Wi-Fi ranging bias are autonomously calibrated to provide a more accurate forward 3D localization performance. In addition, the backward smoothing algorithm and a novel deep-learning model are applied in order to construct an autonomous and efficient crowdsourced Wi-Fi fingerprinting database using the detected quick response (QR) code-based landmarks. Finally, the adaptive extended Kalman filter is adopted to combine the corresponding location sources using different integration models to provide a precise multi-source fusion based multi-floor indoor localization performance. The real-world experiments demonstrate that the presented SM-WRFS is proven to realize precise 3D indoor positioning under different environments, and the meter-level positioning accuracy can be acquired in Wi-Fi ranging supported indoor areas.

Original languageEnglish
Article number5376
JournalRemote Sensing
Issue number21
Publication statusPublished - Nov 2022


  • crowdsourced fingerprinting
  • deep-learning
  • indoor localization
  • low-cost sensors
  • Wi-Fi ranging

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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