An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation
The received signal strength–based fingerprinting navigation system is able to provide location information with accuracies in the meter region under the assistance of inertial measuring units. However, the computational complexity in mobile terminal of this cooperation method is great for real-time...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2017-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717711651 |
| _version_ | 1797764441316524032 |
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| author | Junhua Yang Yong Li Wei Cheng |
| author_facet | Junhua Yang Yong Li Wei Cheng |
| author_sort | Junhua Yang |
| collection | DOAJ |
| description | The received signal strength–based fingerprinting navigation system is able to provide location information with accuracies in the meter region under the assistance of inertial measuring units. However, the computational complexity in mobile terminal of this cooperation method is great for real-time position. The inertial measuring unit has the drawback of error drift, and not all the device has a self-contained unit. In order to obtain high-accurate and continuous navigation information for indoor general devices in small computations, a novel combination of fusing extended Kalman filter and fingerprinting navigation algorithm, including K-nearest neighbor and Pearson correlation coefficient, is proposed in this article. A prototype of the improved system has been worked in a real scenario. A laptop on a four-wheel handcart is moving at a constant speed in a building storey, and the measurement localization is acquired by fingerprinting algorithm during online phase. Meanwhile, the modification localization is produced by extended Kalman filter when the target is moving in the floor. Finally, compared to K-nearest neighbor, Pearson correlation coefficient, and a combination of both, the final modification localization value is more accurate. The results show that the mean error is 53.2%, 51%, and 25.8% lower than the other three methods. |
| first_indexed | 2024-03-12T19:55:44Z |
| format | Article |
| id | doaj.art-37cbc531ce774376a90c15c581437d55 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T19:55:44Z |
| publishDate | 2017-05-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-37cbc531ce774376a90c15c581437d552023-08-02T02:47:47ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772017-05-011310.1177/1550147717711651An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigationJunhua YangYong LiWei ChengThe received signal strength–based fingerprinting navigation system is able to provide location information with accuracies in the meter region under the assistance of inertial measuring units. However, the computational complexity in mobile terminal of this cooperation method is great for real-time position. The inertial measuring unit has the drawback of error drift, and not all the device has a self-contained unit. In order to obtain high-accurate and continuous navigation information for indoor general devices in small computations, a novel combination of fusing extended Kalman filter and fingerprinting navigation algorithm, including K-nearest neighbor and Pearson correlation coefficient, is proposed in this article. A prototype of the improved system has been worked in a real scenario. A laptop on a four-wheel handcart is moving at a constant speed in a building storey, and the measurement localization is acquired by fingerprinting algorithm during online phase. Meanwhile, the modification localization is produced by extended Kalman filter when the target is moving in the floor. Finally, compared to K-nearest neighbor, Pearson correlation coefficient, and a combination of both, the final modification localization value is more accurate. The results show that the mean error is 53.2%, 51%, and 25.8% lower than the other three methods.https://doi.org/10.1177/1550147717711651 |
| spellingShingle | Junhua Yang Yong Li Wei Cheng An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation International Journal of Distributed Sensor Networks |
| title | An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation |
| title_full | An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation |
| title_fullStr | An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation |
| title_full_unstemmed | An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation |
| title_short | An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation |
| title_sort | improved neighbor correlation extended kalman filter fusion method for indoor navigation |
| url | https://doi.org/10.1177/1550147717711651 |
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