Indoor positioning tracking with magnetic field and improved particle filter
The indoor magnetic field is omnipresent and independent from external equipment. Local magnetic field is also relatively stable compared with WiFi signals in the same environment and nonuniform in different locations. However, it has low discernibility, in that there are similar magnetic features i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717741835 |
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| _version_ | 1849702380234342400 |
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| author | Mei Zhang Tingting Qing Jinhui Zhu Wenbo Shen |
| author_facet | Mei Zhang Tingting Qing Jinhui Zhu Wenbo Shen |
| author_sort | Mei Zhang |
| collection | DOAJ |
| description | The indoor magnetic field is omnipresent and independent from external equipment. Local magnetic field is also relatively stable compared with WiFi signals in the same environment and nonuniform in different locations. However, it has low discernibility, in that there are similar magnetic features in different areas. Pedestrian movement model is a continuous navigation method based on inertial sensors. However, inertial sensors provide only short-term accuracy and suffer from accumulation error. Hence, an indoor positioning tracking that uses the magnetic field and an improved particle filter is proposed in this article. First, adaptive four-threshold step-detection and mixed adaptive step length methods are used to obtain the travel distance in different walking states. Furthermore, an improved particle filter is adopted to calibrate the pedestrian movement model by fusing indoor magnetic field information. Besides, initial locations of particles are restricted in a determined area according to WiFi signals, and the diversity of the particles is increased by a classified heuristic resampling. The proposed system was implemented on an Android phone and extensive experiments were conducted in real indoor environments. The experiments show that the positioning accuracy and system robustness are greatly improved compared with other methods. |
| format | Article |
| id | doaj-art-23497bcb45f84f75b5e020c443e6fd25 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-23497bcb45f84f75b5e020c443e6fd252025-08-20T03:17:40ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-11-011310.1177/1550147717741835Indoor positioning tracking with magnetic field and improved particle filterMei Zhang0Tingting Qing1Jinhui Zhu2Wenbo Shen3Engineering Research Centre for Precision Electronic Manufacturing Equipment of Ministry of Education, South China University of Technology, Guangzhou, ChinaEngineering Research Centre for Precision Electronic Manufacturing Equipment of Ministry of Education, South China University of Technology, Guangzhou, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou, ChinaEngineering Research Centre for Precision Electronic Manufacturing Equipment of Ministry of Education, South China University of Technology, Guangzhou, ChinaThe indoor magnetic field is omnipresent and independent from external equipment. Local magnetic field is also relatively stable compared with WiFi signals in the same environment and nonuniform in different locations. However, it has low discernibility, in that there are similar magnetic features in different areas. Pedestrian movement model is a continuous navigation method based on inertial sensors. However, inertial sensors provide only short-term accuracy and suffer from accumulation error. Hence, an indoor positioning tracking that uses the magnetic field and an improved particle filter is proposed in this article. First, adaptive four-threshold step-detection and mixed adaptive step length methods are used to obtain the travel distance in different walking states. Furthermore, an improved particle filter is adopted to calibrate the pedestrian movement model by fusing indoor magnetic field information. Besides, initial locations of particles are restricted in a determined area according to WiFi signals, and the diversity of the particles is increased by a classified heuristic resampling. The proposed system was implemented on an Android phone and extensive experiments were conducted in real indoor environments. The experiments show that the positioning accuracy and system robustness are greatly improved compared with other methods.https://doi.org/10.1177/1550147717741835 |
| spellingShingle | Mei Zhang Tingting Qing Jinhui Zhu Wenbo Shen Indoor positioning tracking with magnetic field and improved particle filter International Journal of Distributed Sensor Networks |
| title | Indoor positioning tracking with magnetic field and improved particle filter |
| title_full | Indoor positioning tracking with magnetic field and improved particle filter |
| title_fullStr | Indoor positioning tracking with magnetic field and improved particle filter |
| title_full_unstemmed | Indoor positioning tracking with magnetic field and improved particle filter |
| title_short | Indoor positioning tracking with magnetic field and improved particle filter |
| title_sort | indoor positioning tracking with magnetic field and improved particle filter |
| url | https://doi.org/10.1177/1550147717741835 |
| work_keys_str_mv | AT meizhang indoorpositioningtrackingwithmagneticfieldandimprovedparticlefilter AT tingtingqing indoorpositioningtrackingwithmagneticfieldandimprovedparticlefilter AT jinhuizhu indoorpositioningtrackingwithmagneticfieldandimprovedparticlefilter AT wenboshen indoorpositioningtrackingwithmagneticfieldandimprovedparticlefilter |