Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

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.

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

“…Furthermore, the advantage of using Bluetooth in positioning systems lies in its high security, low-cost, low-power and small size (Mautz, 2012;Gu et al, 2009;Zhuang et al, 2016). However, device discovery is inherently a slow process in the Bluetooth-based positioning methods, thus affecting real-time positioning, accuracy and performance, thereby making Bluetooth undesirable for tracking and navigation purposes (Feldmann et al, 2003;Bekkelien, 2012).…”

“…Gomez et al (2012), Y. Wang et al (2013), Rida, Liu, Jadi, Algawhari, and Askourih (2015) and Zhuang, Yang, Li, Qi, and El-Sheimy (2016), among other researchers, have used mobile WPAN that utilise the RSS feature in a couple of implementations that include positioning. Bluetooth-based positioning system is a system that locate and track objects and people inside a building by providing real-time position information of radio and mobile phone users using fixed Bluetooth sensors connected to a LAN (Y.…”

“…Bluetooth-based positioning system is a system that locate and track objects and people inside a building by providing real-time position information of radio and mobile phone users using fixed Bluetooth sensors connected to a LAN (Y. Wang et al, 2013;Zhuang et al, 2016). The positioning system consists of Bluetooth devices, Bluetooth tags or sensors, server and WLAN (H. Liu et al, 2007;Deak et al, 2012).…”

The Internet of Things – Improving Customer Shopping Experience at Stores

“…Figure : The Process of BLE Beacon technique for in-store analytics Phase 1: Design a Bluetooth Low Energy beacon Bluetooth Low Energy (BLE) [3] signals from battery driven beacons are at the core of the indoor location technology. It"s one of the latest technologies that has emerged and become an industry standard available on most devices today.…”

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

“…The currently available indoor location systems, such as Wi-Fi [2,3], Radiofrequency Identification (RFID) [4][5][6], Ultra-WideBand (UWB) [7][8][9][10][11], Bluetooth [12][13][14], and Ultrasound [15,16], have achieved great progress in terms of transferring data within a short range, but when it comes to the indoor environment, these systems have various problems, including high cost, high power consumption, low accuracy, and low precision. The advent of Bluetooth Low Energy (BLE) provided opportunities to overcome the above problems.…”