TraIL: Pinpoint Trajectory for Indoor Localization
Indoor localization on smartphones is an enabler for a number of ubiquitous and mobile computing applications attracting worldwide attentions. Many location-based services rely on WiFi fingerprinting approaches to achieve a reasonable accuracy. However, there is still room for improvement due to the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/372425 |
| _version_ | 1826993133564461056 |
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| author | Shengnan Li Zheng Qin Chenshu Wu Zheng Yang |
| author_facet | Shengnan Li Zheng Qin Chenshu Wu Zheng Yang |
| author_sort | Shengnan Li |
| collection | DOAJ |
| description | Indoor localization on smartphones is an enabler for a number of ubiquitous and mobile computing applications attracting worldwide attentions. Many location-based services rely on WiFi fingerprinting approaches to achieve a reasonable accuracy. However, there is still room for improvement due to the prevalent-existing errors (e.g., 8∼12 m). In this study, we devise and implement a high-accuracy indoor localization solution leveraging the WiFi-based method and pedestrian mobility provided by smartphones. Our basic idea is that WiFi-only localization can generate rough but absolute positions, while user motion is able to bring accurate but relative locations. Taking both sides into account simultaneously, we design techniques to refine the raw WiFi positions in the process of laying the precise local trajectory appropriately down to the absolute coordinate using a novel least median of squares (LMS) fit algorithm. We develop a prototype system, named TraIL, and conduct comprehensive experiments in a building along different shaped routes. The evaluation results show that TraIL can achieve 80% improvement on average error with respect to WiFi-only indoor localization. |
| first_indexed | 2024-03-12T20:16:56Z |
| format | Article |
| id | doaj.art-d9d006af703245538d8614ce11377b71 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2025-02-18T08:59:09Z |
| publishDate | 2015-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-d9d006af703245538d8614ce11377b712024-11-02T23:53:33ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-11-011110.1155/2015/372425372425TraIL: Pinpoint Trajectory for Indoor LocalizationShengnan LiZheng QinChenshu WuZheng YangIndoor localization on smartphones is an enabler for a number of ubiquitous and mobile computing applications attracting worldwide attentions. Many location-based services rely on WiFi fingerprinting approaches to achieve a reasonable accuracy. However, there is still room for improvement due to the prevalent-existing errors (e.g., 8∼12 m). In this study, we devise and implement a high-accuracy indoor localization solution leveraging the WiFi-based method and pedestrian mobility provided by smartphones. Our basic idea is that WiFi-only localization can generate rough but absolute positions, while user motion is able to bring accurate but relative locations. Taking both sides into account simultaneously, we design techniques to refine the raw WiFi positions in the process of laying the precise local trajectory appropriately down to the absolute coordinate using a novel least median of squares (LMS) fit algorithm. We develop a prototype system, named TraIL, and conduct comprehensive experiments in a building along different shaped routes. The evaluation results show that TraIL can achieve 80% improvement on average error with respect to WiFi-only indoor localization.https://doi.org/10.1155/2015/372425 |
| spellingShingle | Shengnan Li Zheng Qin Chenshu Wu Zheng Yang TraIL: Pinpoint Trajectory for Indoor Localization International Journal of Distributed Sensor Networks |
| title | TraIL: Pinpoint Trajectory for Indoor Localization |
| title_full | TraIL: Pinpoint Trajectory for Indoor Localization |
| title_fullStr | TraIL: Pinpoint Trajectory for Indoor Localization |
| title_full_unstemmed | TraIL: Pinpoint Trajectory for Indoor Localization |
| title_short | TraIL: Pinpoint Trajectory for Indoor Localization |
| title_sort | trail pinpoint trajectory for indoor localization |
| url | https://doi.org/10.1155/2015/372425 |
| work_keys_str_mv | AT shengnanli trailpinpointtrajectoryforindoorlocalization AT zhengqin trailpinpointtrajectoryforindoorlocalization AT chenshuwu trailpinpointtrajectoryforindoorlocalization AT zhengyang trailpinpointtrajectoryforindoorlocalization |