An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter
Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location....
| Autori principali: | , , , , |
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| Natura: | Articolo |
| Lingua: | English |
| Pubblicazione: |
MDPI AG
2018-08-01
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| Serie: | ISPRS International Journal of Geo-Information |
| Soggetti: | |
| Accesso online: | http://www.mdpi.com/2220-9964/7/8/324 |
| _version_ | 1828801983021056000 |
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| author | Jian Chen Gang Ou Ao Peng Lingxiang Zheng Jianghong Shi |
| author_facet | Jian Chen Gang Ou Ao Peng Lingxiang Zheng Jianghong Shi |
| author_sort | Jian Chen |
| collection | DOAJ |
| description | Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m. |
| first_indexed | 2024-12-12T06:55:55Z |
| format | Article |
| id | doaj.art-65b2e70ddb82483787bab0f9c1d4d194 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-12T06:55:55Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-65b2e70ddb82483787bab0f9c1d4d1942022-12-22T00:33:57ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-08-017832410.3390/ijgi7080324ijgi7080324An INS/Floor-Plan Indoor Localization System Using the Firefly Particle FilterJian Chen0Gang Ou1Ao Peng2Lingxiang Zheng3Jianghong Shi4School of Information Science and Engineering, Xiamen University, Xiamen 361001, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361001, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361001, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361001, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361001, ChinaLocation-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.http://www.mdpi.com/2220-9964/7/8/324indoor localization systemINSfloor planKFFPF |
| spellingShingle | Jian Chen Gang Ou Ao Peng Lingxiang Zheng Jianghong Shi An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter ISPRS International Journal of Geo-Information indoor localization system INS floor plan KF FPF |
| title | An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter |
| title_full | An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter |
| title_fullStr | An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter |
| title_full_unstemmed | An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter |
| title_short | An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter |
| title_sort | ins floor plan indoor localization system using the firefly particle filter |
| topic | indoor localization system INS floor plan KF FPF |
| url | http://www.mdpi.com/2220-9964/7/8/324 |
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