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Chen G, Meng X, Wang Y, et al. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization. Sensors (Basel). 2015;15(9):24595-614doi: 10.3390/s150924595.
Chen, G., Meng, X., Wang, Y., Zhang, Y., Tian, P., & Yang, H. (2015). Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization. Sensors (Basel, Switzerland), 15(9), 24595-614. https://doi.org/10.3390/s150924595
Chen, Guoliang, et al. "Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization." Sensors (Basel, Switzerland) vol. 15,9 (2015): 24595-614. doi: https://doi.org/10.3390/s150924595
Chen G, Meng X, Wang Y, Zhang Y, Tian P, Yang H. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization. Sensors (Basel). 2015 Sep 23;15(9):24595-614. doi: 10.3390/s150924595. PMID: 26404314; PMCID: PMC4610469.
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Sensors (Basel). 2015 Sep 23;15(9):24595-614. doi: 10.3390/s150924595.

Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization.

Sensors (Basel, Switzerland)

Guoliang Chen, Xiaolin Meng, Yunjia Wang, Yanzhe Zhang, Peng Tian, Huachao Yang

Affiliations

  1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, 1 Daxue Road, 221116 Xuzhou, China. [email protected].
  2. Nottingham Geospatial Institute, University of Nottingham, Triumph Road, NG7 2TU Nottingham, UK. [email protected].
  3. School of Environment Science and Spatial Informatics, China University of Mining and Technology, 1 Daxue Road, 221116 Xuzhou, China. [email protected].
  4. School of Environment Science and Spatial Informatics, China University of Mining and Technology, 1 Daxue Road, 221116 Xuzhou, China. [email protected].
  5. School of Environment Science and Spatial Informatics, China University of Mining and Technology, 1 Daxue Road, 221116 Xuzhou, China. [email protected].
  6. School of Environment Science and Spatial Informatics, China University of Mining and Technology, 1 Daxue Road, 221116 Xuzhou, China. [email protected].

PMID: 26404314 PMCID: PMC4610469 DOI: 10.3390/s150924595

Abstract

Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.

Keywords: Unity 3D; Unscented Kalman Filter; WiFi/PDR; auto-correlation analysis; clustering; indoor localization

References

  1. Sensors (Basel). 2012;12(12):17208-33 - PubMed
  2. Sensors (Basel). 2015 Jan 05;15(1):715-32 - PubMed
  3. Sensors (Basel). 2015 Mar 04;15(3):5311-30 - PubMed
  4. Sensors (Basel). 2015 Apr 10;15(4):8358-81 - PubMed

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