Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (F...

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Main Authors: Yuan Zhuang, Jun Yang, You Li, Longning Qi, Naser El-Sheimy
Format: Article
Language:English
Published: MDPI AG 2016-04-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/16/5/596
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spelling doaj-44ff4adf69d145a7a9910854038f9dd22020-11-25T01:29:28ZengMDPI AGSensors1424-82202016-04-0116559610.3390/s16050596s16050596Smartphone-Based Indoor Localization with Bluetooth Low Energy BeaconsYuan Zhuang0Jun Yang1You Li2Longning Qi3Naser El-Sheimy4National ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaDepartment of Geomatics Engineering, The University of Calgary, 2500 University Drive, NW, Calgary, AB T2N 1N4, CanadaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaDepartment of Geomatics Engineering, The University of Calgary, 2500 University Drive, NW, Calgary, AB T2N 1N4, CanadaIndoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.http://www.mdpi.com/1424-8220/16/5/596indoor localizationpolynomial regression modelfingerprintingextended Kalman filteringoutlier detectionBLE beacons
collection DOAJ
language English
format Article
sources DOAJ
author Yuan Zhuang
Jun Yang
You Li
Longning Qi
Naser El-Sheimy
spellingShingle Yuan Zhuang
Jun Yang
You Li
Longning Qi
Naser El-Sheimy
Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
Sensors
indoor localization
polynomial regression model
fingerprinting
extended Kalman filtering
outlier detection
BLE beacons
author_facet Yuan Zhuang
Jun Yang
You Li
Longning Qi
Naser El-Sheimy
author_sort Yuan Zhuang
title Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
title_short Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
title_full Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
title_fullStr Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
title_full_unstemmed Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons
title_sort smartphone-based indoor localization with bluetooth low energy beacons
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2016-04-01
description Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.
topic indoor localization
polynomial regression model
fingerprinting
extended Kalman filtering
outlier detection
BLE beacons
url http://www.mdpi.com/1424-8220/16/5/596
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