An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning
In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches h...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/6/1106 |
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| author | Zhenbing Zhang Jingbin Liu Lei Wang Guangyi Guo Xingyu Zheng Xiaodong Gong Sheng Yang Gege Huang |
| author_facet | Zhenbing Zhang Jingbin Liu Lei Wang Guangyi Guo Xingyu Zheng Xiaodong Gong Sheng Yang Gege Huang |
| author_sort | Zhenbing Zhang |
| collection | DOAJ |
| description | In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness. |
| first_indexed | 2024-03-10T13:15:30Z |
| format | Article |
| id | doaj.art-7da90e2dd9904c24af7d8873c696f97b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:15:30Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7da90e2dd9904c24af7d8873c696f97b2023-11-21T10:27:43ZengMDPI AGRemote Sensing2072-42922021-03-01136110610.3390/rs13061106An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine LearningZhenbing Zhang0Jingbin Liu1Lei Wang2Guangyi Guo3Xingyu Zheng4Xiaodong Gong5Sheng Yang6Gege Huang7State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaIn smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.https://www.mdpi.com/2072-4292/13/6/1106smartphoneindoor positioningoutlier detection and removalPDRWiFiKalman Filter |
| spellingShingle | Zhenbing Zhang Jingbin Liu Lei Wang Guangyi Guo Xingyu Zheng Xiaodong Gong Sheng Yang Gege Huang An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning Remote Sensing smartphone indoor positioning outlier detection and removal PDR WiFi Kalman Filter |
| title | An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning |
| title_full | An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning |
| title_fullStr | An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning |
| title_full_unstemmed | An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning |
| title_short | An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning |
| title_sort | enhanced smartphone indoor positioning scheme with outlier removal using machine learning |
| topic | smartphone indoor positioning outlier detection and removal PDR WiFi Kalman Filter |
| url | https://www.mdpi.com/2072-4292/13/6/1106 |
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