A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/12/2030 |
| _version_ | 1798006645402370048 |
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| author | Yi Lu Dongyan Wei Qifeng Lai Wen Li Hong Yuan |
| author_facet | Yi Lu Dongyan Wei Qifeng Lai Wen Li Hong Yuan |
| author_sort | Yi Lu |
| collection | DOAJ |
| description | Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time. |
| first_indexed | 2024-04-11T12:57:54Z |
| format | Article |
| id | doaj.art-11554780490e42d1a63105a2ba71ac67 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:57:54Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-11554780490e42d1a63105a2ba71ac672022-12-22T04:23:01ZengMDPI AGSensors1424-82202016-11-011612203010.3390/s16122030s16122030A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov ModelYi Lu0Dongyan Wei1Qifeng Lai2Wen Li3Hong Yuan4Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, ChinaAcademy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, ChinaAcademy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, ChinaAcademy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, ChinaAcademy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, ChinaIndoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.http://www.mdpi.com/1424-8220/16/12/2030PDRcontext recognitionHMMindoor localizationturn detection |
| spellingShingle | Yi Lu Dongyan Wei Qifeng Lai Wen Li Hong Yuan A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model Sensors PDR context recognition HMM indoor localization turn detection |
| title | A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model |
| title_full | A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model |
| title_fullStr | A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model |
| title_full_unstemmed | A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model |
| title_short | A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model |
| title_sort | context recognition aided pdr localization method based on the hidden markov model |
| topic | PDR context recognition HMM indoor localization turn detection |
| url | http://www.mdpi.com/1424-8220/16/12/2030 |
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