An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/24/7269 |
| _version_ | 1797544263402127360 |
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| author | Ling Ruan Ling Zhang Tong Zhou Yi Long |
| author_facet | Ling Ruan Ling Zhang Tong Zhou Yi Long |
| author_sort | Ling Ruan |
| collection | DOAJ |
| description | The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased. |
| first_indexed | 2024-03-10T13:58:12Z |
| format | Article |
| id | doaj.art-77505779e6414f42b028136223d04c87 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:58:12Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-77505779e6414f42b028136223d04c872023-11-21T01:26:22ZengMDPI AGSensors1424-82202020-12-012024726910.3390/s20247269An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint WindowLing Ruan0Ling Zhang1Tong Zhou2Yi Long3Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaThe weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.https://www.mdpi.com/1424-8220/20/24/7269Bluetooth positioningfingerprint windowdynamic windowpositioning efficiency |
| spellingShingle | Ling Ruan Ling Zhang Tong Zhou Yi Long An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window Sensors Bluetooth positioning fingerprint window dynamic window positioning efficiency |
| title | An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window |
| title_full | An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window |
| title_fullStr | An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window |
| title_full_unstemmed | An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window |
| title_short | An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window |
| title_sort | improved bluetooth indoor positioning method using dynamic fingerprint window |
| topic | Bluetooth positioning fingerprint window dynamic window positioning efficiency |
| url | https://www.mdpi.com/1424-8220/20/24/7269 |
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