Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor
The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/18/5416 |
| _version_ | 1827705767898120192 |
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| author | Zhengwu Yuan Xupeng Zha Xiaojian Zhang |
| author_facet | Zhengwu Yuan Xupeng Zha Xiaojian Zhang |
| author_sort | Zhengwu Yuan |
| collection | DOAJ |
| description | The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients <i>K</i>-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m. |
| first_indexed | 2024-03-10T16:09:27Z |
| format | Article |
| id | doaj.art-2e1ffa7c2c4946b795931a900bf46d89 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:09:27Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2e1ffa7c2c4946b795931a900bf46d892023-11-20T14:32:39ZengMDPI AGSensors1424-82202020-09-012018541610.3390/s20185416Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest NeighborZhengwu Yuan0Xupeng Zha1Xiaojian Zhang2School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaThe complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients <i>K</i>-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.https://www.mdpi.com/1424-8220/20/18/5416multi-type fingerprintsindoor positioning and localizationmulti-task learningweight coefficients <i>K</i>-Nearest neighbor |
| spellingShingle | Zhengwu Yuan Xupeng Zha Xiaojian Zhang Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor Sensors multi-type fingerprints indoor positioning and localization multi-task learning weight coefficients <i>K</i>-Nearest neighbor |
| title | Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor |
| title_full | Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor |
| title_fullStr | Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor |
| title_full_unstemmed | Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor |
| title_short | Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients <i>K</i>-Nearest Neighbor |
| title_sort | adaptive multi type fingerprint indoor positioning and localization method based on multi task learning and weight coefficients i k i nearest neighbor |
| topic | multi-type fingerprints indoor positioning and localization multi-task learning weight coefficients <i>K</i>-Nearest neighbor |
| url | https://www.mdpi.com/1424-8220/20/18/5416 |
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