Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation
This paper investigates the indoor position tracking problem under the variation of received signal strength (RSS) characteristic from the changes of device statuses and environmental factors. A novel indoor position tracking algorithm is introduced to provide reliable position estimates by integrat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/674635 |
| _version_ | 1797722098046599168 |
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| author | Sangwoo Lee Bongkwan Cho Bonhyun Koo Sanghwan Ryu Jaehoon Choi Sunwoo Kim |
| author_facet | Sangwoo Lee Bongkwan Cho Bonhyun Koo Sanghwan Ryu Jaehoon Choi Sunwoo Kim |
| author_sort | Sangwoo Lee |
| collection | DOAJ |
| description | This paper investigates the indoor position tracking problem under the variation of received signal strength (RSS) characteristic from the changes of device statuses and environmental factors. A novel indoor position tracking algorithm is introduced to provide reliable position estimates by integrating motion sensor-based positioning (i.e., dead-reckoning) and RSS-based fingerprinting positioning with Kalman filter. In the presence of the RSS variation, RSS-based fingerprinting positioning provides unreliable results due to different characteristics of RSS measurements in the offline and online phases, and the tracking performance is degraded. To mitigate the effect of the RSS variation, a recursive least square estimation-based self-calibration algorithm is proposed that estimates the RSS variation parameters and provides the mapping between the offline and online RSS measurements. By combining the Kalman filter-based tracking algorithm with the self-calibration, the proposed algorithm can achieve higher tracking accuracy even in severe RSS variation conditions. Through extensive computer simulations, we have shown that the proposed algorithm outperforms other position tracking algorithms without self-calibration. |
| first_indexed | 2024-03-12T09:43:39Z |
| format | Article |
| id | doaj.art-ea48513e772b43d684818f5016765b0b |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T09:43:39Z |
| publishDate | 2015-08-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-ea48513e772b43d684818f5016765b0b2023-09-02T13:06:44ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/674635674635Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation MitigationSangwoo Lee0Bongkwan Cho1Bonhyun Koo2Sanghwan Ryu3Jaehoon Choi4Sunwoo Kim5 Department of Electronics and Computer Engineering, Hanyang University, Seoul 133-791, Republic of Korea Korea Railroad Research Institute, Uiwang 437-757, Republic of Korea Department of Electronics and Computer Engineering, Hanyang University, Seoul 133-791, Republic of Korea Korea Railroad Research Institute, Uiwang 437-757, Republic of Korea Department of Electronics and Computer Engineering, Hanyang University, Seoul 133-791, Republic of Korea Department of Electronics and Computer Engineering, Hanyang University, Seoul 133-791, Republic of KoreaThis paper investigates the indoor position tracking problem under the variation of received signal strength (RSS) characteristic from the changes of device statuses and environmental factors. A novel indoor position tracking algorithm is introduced to provide reliable position estimates by integrating motion sensor-based positioning (i.e., dead-reckoning) and RSS-based fingerprinting positioning with Kalman filter. In the presence of the RSS variation, RSS-based fingerprinting positioning provides unreliable results due to different characteristics of RSS measurements in the offline and online phases, and the tracking performance is degraded. To mitigate the effect of the RSS variation, a recursive least square estimation-based self-calibration algorithm is proposed that estimates the RSS variation parameters and provides the mapping between the offline and online RSS measurements. By combining the Kalman filter-based tracking algorithm with the self-calibration, the proposed algorithm can achieve higher tracking accuracy even in severe RSS variation conditions. Through extensive computer simulations, we have shown that the proposed algorithm outperforms other position tracking algorithms without self-calibration.https://doi.org/10.1155/2015/674635 |
| spellingShingle | Sangwoo Lee Bongkwan Cho Bonhyun Koo Sanghwan Ryu Jaehoon Choi Sunwoo Kim Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation International Journal of Distributed Sensor Networks |
| title | Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation |
| title_full | Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation |
| title_fullStr | Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation |
| title_full_unstemmed | Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation |
| title_short | Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation |
| title_sort | kalman filter based indoor position tracking with self calibration for rss variation mitigation |
| url | https://doi.org/10.1155/2015/674635 |
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