Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons

As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately...

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Main Authors: Wuping Liu, Wei Guo, Xinyan Zhu
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/10/8/526
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spelling doaj-cf95435206ce4f7a9538c864bbec3d462021-08-26T13:50:53ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-08-011052652610.3390/ijgi10080526Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE BeaconsWuping Liu0Wei Guo1Xinyan Zhu2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaAs communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.https://www.mdpi.com/2220-9964/10/8/526map-aidedbluetooth low energycell areaoptimal reference beacon
collection DOAJ
language English
format Article
sources DOAJ
author Wuping Liu
Wei Guo
Xinyan Zhu
spellingShingle Wuping Liu
Wei Guo
Xinyan Zhu
Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons
ISPRS International Journal of Geo-Information
map-aided
bluetooth low energy
cell area
optimal reference beacon
author_facet Wuping Liu
Wei Guo
Xinyan Zhu
author_sort Wuping Liu
title Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons
title_short Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons
title_full Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons
title_fullStr Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons
title_full_unstemmed Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons
title_sort map-aided indoor positioning algorithm with complex deployed ble beacons
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2021-08-01
description As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.
topic map-aided
bluetooth low energy
cell area
optimal reference beacon
url https://www.mdpi.com/2220-9964/10/8/526
work_keys_str_mv AT wupingliu mapaidedindoorpositioningalgorithmwithcomplexdeployedblebeacons
AT weiguo mapaidedindoorpositioningalgorithmwithcomplexdeployedblebeacons
AT xinyanzhu mapaidedindoorpositioningalgorithmwithcomplexdeployedblebeacons
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