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Sensors 2016, 16(5), 596; doi:10.3390/s16050596

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

1
National ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, China
2
Department of Geomatics Engineering, The University of Calgary, 2500 University Drive, NW, Calgary, AB T2N 1N4, Canada
3
GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Mihai Lazarescu and Luciano Lavagno
Received: 9 March 2016 / Revised: 14 April 2016 / Accepted: 20 April 2016 / Published: 26 April 2016
(This article belongs to the Special Issue Data in the IoT: from Sensing to Meaning)
View Full-Text   |   Download PDF [8726 KB, uploaded 26 April 2016]   |  

Abstract

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. View Full-Text
Keywords: indoor localization; polynomial regression model; fingerprinting; extended Kalman filtering; outlier detection; BLE beacons indoor localization; polynomial regression model; fingerprinting; extended Kalman filtering; outlier detection; BLE beacons
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MDPI and ACS Style

Zhuang, Y.; Yang, J.; Li, Y.; Qi, L.; El-Sheimy, N. Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons. Sensors 2016, 16, 596.

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