A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things
As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the grow...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-09-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720961235 |
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| author | Nan Hu Chuan Lin Fangjun Luan Chengdong Wu Qi Song Li Chen |
| author_facet | Nan Hu Chuan Lin Fangjun Luan Chengdong Wu Qi Song Li Chen |
| author_sort | Nan Hu |
| collection | DOAJ |
| description | As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error. |
| format | Article |
| id | doaj-art-95457a06baf54228bbb241bd05bd68ea |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-95457a06baf54228bbb241bd05bd68ea2025-02-03T01:30:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-09-011610.1177/1550147720961235A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of thingsNan Hu0Chuan Lin1Fangjun Luan2Chengdong Wu3Qi Song4Li Chen5Information & Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, ChinaSoft college of Northeastern University, Shenyang, ChinaInformation & Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, ChinaFaculty of Robot Science and Engineering, Northeastern University, Shenyang, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang, ChinaArchitectural Design and Research Institute, Shenyang Jianzhu University, Shenyang, ChinaAs the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.https://doi.org/10.1177/1550147720961235 |
| spellingShingle | Nan Hu Chuan Lin Fangjun Luan Chengdong Wu Qi Song Li Chen A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things International Journal of Distributed Sensor Networks |
| title | A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things |
| title_full | A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things |
| title_fullStr | A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things |
| title_full_unstemmed | A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things |
| title_short | A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things |
| title_sort | mobile localization method based on a robust extend kalman filter and improved m estimation in internet of things |
| url | https://doi.org/10.1177/1550147720961235 |
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