Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization
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 t...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
MDPI
2015
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/35629/ |
| _version_ | 1848795124155285504 |
|---|---|
| author | Chen, Guoliang Meng, Xiaolin Wang, Yunjia Zhang, Yanzhe Tian, Peng |
| author_facet | Chen, Guoliang Meng, Xiaolin Wang, Yunjia Zhang, Yanzhe Tian, Peng |
| author_sort | Chen, Guoliang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:27:06Z |
| format | Article |
| id | nottingham-35629 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:27:06Z |
| publishDate | 2015 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-356292020-05-04T17:16:32Z https://eprints.nottingham.ac.uk/35629/ Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization Chen, Guoliang Meng, Xiaolin Wang, Yunjia Zhang, Yanzhe Tian, Peng 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. MDPI 2015-09-23 Article PeerReviewed Chen, Guoliang, Meng, Xiaolin, Wang, Yunjia, Zhang, Yanzhe and Tian, Peng (2015) Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization. Sensors, 15 (9). pp. 24595-24614. ISSN 1424-8220 Indoor localization; WiFi/PDR; Clustering; Auto-correlation analysis; Unscented Kalman Filter; Unity 3D http://www.mdpi.com/1424-8220/15/9/24595 doi:10.3390/s150924595 doi:10.3390/s150924595 |
| spellingShingle | Indoor localization; WiFi/PDR; Clustering; Auto-correlation analysis; Unscented Kalman Filter; Unity 3D Chen, Guoliang Meng, Xiaolin Wang, Yunjia Zhang, Yanzhe Tian, Peng Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization |
| title | Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization |
| title_full | Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization |
| title_fullStr | Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization |
| title_full_unstemmed | Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization |
| title_short | Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization |
| title_sort | integrated wifi/pdr/smartphone using an unscented kalman filter algorithm for 3d indoor localization |
| topic | Indoor localization; WiFi/PDR; Clustering; Auto-correlation analysis; Unscented Kalman Filter; Unity 3D |
| url | https://eprints.nottingham.ac.uk/35629/ https://eprints.nottingham.ac.uk/35629/ https://eprints.nottingham.ac.uk/35629/ |