An INS/WiFi Indoor Localization System Based on the Weighted Least Squares
For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a wi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/5/1458 |
| _version_ | 1798026751189712896 |
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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 | For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m. |
| first_indexed | 2024-04-11T18:41:52Z |
| format | Article |
| id | doaj.art-3d43b2388a384150bf81643b3ad5f7ff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:41:52Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3d43b2388a384150bf81643b3ad5f7ff2022-12-22T04:08:58ZengMDPI AGSensors1424-82202018-05-01185145810.3390/s18051458s18051458An INS/WiFi Indoor Localization System Based on the Weighted Least SquaresJian 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, ChinaFor smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.http://www.mdpi.com/1424-8220/18/5/1458INSWiFi fingerprintpre-processing techniquesMDTWWLS |
| spellingShingle | Jian Chen Gang Ou Ao Peng Lingxiang Zheng Jianghong Shi An INS/WiFi Indoor Localization System Based on the Weighted Least Squares Sensors INS WiFi fingerprint pre-processing techniques MDTW WLS |
| title | An INS/WiFi Indoor Localization System Based on the Weighted Least Squares |
| title_full | An INS/WiFi Indoor Localization System Based on the Weighted Least Squares |
| title_fullStr | An INS/WiFi Indoor Localization System Based on the Weighted Least Squares |
| title_full_unstemmed | An INS/WiFi Indoor Localization System Based on the Weighted Least Squares |
| title_short | An INS/WiFi Indoor Localization System Based on the Weighted Least Squares |
| title_sort | ins wifi indoor localization system based on the weighted least squares |
| topic | INS WiFi fingerprint pre-processing techniques MDTW WLS |
| url | http://www.mdpi.com/1424-8220/18/5/1458 |
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