Technological Viability Assessment of Bluetooth Low Energy Technology for Indoor Localization

Topak, Fatih; Pekeriçli, Mehmet Koray; Tanyer, Ali Murat

doi:10.1061/(asce)cp.1943-5487.0000778

Cited by 33 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 3: Then we sort the n distance d i in ascending order and choose the first K items to calculate the target position by averaging the K corresponding coordinates as shown in Equation (4).…”

Section: Ble Position Technologymentioning

confidence: 99%

“…However, the accuracy deteriorates significantly because GNSS signals are unreliable or blocked in indoor environments. To provide a reliable, stable position service in indoor environments, many types of indoor positioning technologies such as wireless fidelity (Wi-Fi) [1][2][3], Bluetooth low energy (BLE) beacons [4,5], radio frequency identification (RFID) [6,7], ultrasonic [8], infrared [9], ultra-wideband (UWB) [10,11], pseudolite [12,13], computer vision [14,15] had been proposed by experts and scholars.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Wang

Sun

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.

show abstract

Section: Ble Position Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Wang

Sun

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Bluetooth Low Energy (BLE) technology has been used in recent years in diverse areas [26]- [31]; this technology has shown to be suitable for devices that require a long battery life rather than high speed data transfer [28]. The main benefit of this approach is to achieve simpler, lower cost and lower power consumption wireless devices [32].…”

Section: Related Workmentioning

confidence: 99%

An Outdoor Navigation Assistance System for Visually Impaired People in Public Transportation

et al. 2021

View full text Add to dashboard Cite

Visually impaired and blind people (VIBP) have to face significant difficulties on trying to locate public transport vehicles and bus stops due to their vision restrictions. Over the past decade, diverse assistance systems have been developed to solve this problem. However, most of them are based upon the global positioning system (GPS) and present satellite coverage problems in indoor environments. Some others are wearable prototypes that turn out to be onerous for the user. This paper presents an assistance system for VIBP in the use of public transportation. The proposed system uses Bluetooth Low Energy (BLE) technology for location and communication purposes and a developed mobile application for user-smartphone interaction. The BLE beacons are installed on buses and their stops; accordingly, the mobile application tracks them in real-time and provides the relevant information to the user by means of verbal instructions; transportation line, destination, next stop name, and current location. This information allows the user to properly select the desired bus in advance and get off at the correct destination stop. The proposed system has been tested within two different scenarios; (1) under controlled conditions and (2) in a real environment. The results show that the proposed system is 97.6% effective when VIBP travel from one point to another independently. In addition, according to an assessment sheet completed by the participants, the proposed system grants them greater confidence and independence than GPS-based systems because of the following reasons; firstly, it can work with internet connection or without internet connection. Secondly, it is not an onerous system; information about the location of vehicles and stops is provided in real-time. Last but not least, it does not present satellite coverage problems in indoor environments. INDEX TERMSAssistive technology, visually impaired people, blind passenger, mobile application, public transport, bluetooth low energy technology I. SALVADOR MARTÍNEZ-CRUZ received the degree in Electromechanical Engineering and the M.Sc. degree in Mechatronics from the Faculty of Engineering, University Autonomous of Queretaro, Mexico, in 2016 and 2019, respectively. He is currently working towards the Ph.D. degree at the Autonomous University of Queretaro with the department of Mechatronics, Mexico. His research interests are in the fields of image processing, signal processing, and mechatronics. LUIS A. MORALES-HERNÁNDEZ received the degree in electromechanical engineering, the M.Sc. degree in instrumentation and automatic control, and the Ph.D. degree in engineering from the Faculty of Engineering, University Autonomous of Queretaro, Mexico, in 2004, 2005, and 2009, respectively. He is currently a Head Professor with the Faculty of Engineering, Autonomous University of Queretaro, where he holds electromechanical engineering and mechatronics master's and doctoral positions, respectively. He is involved in some governmental projects and technology transfer contracts to in...

show abstract

“…However, highprecision indoor positioning based on GNSS has not been achieved since indoor GNSS signals are usually weak or even nonexistent. Therefore, some positioning technologies such as ultrawideband (UWB) [1,2], Bluetooth [3], WiFi [4,5], radio frequency identification (RFID) [6,7], computer vision [7], ultrasonic [8], inertial navigation system (INS) [9], pseudolite [10], and geomagnetic fields [11] were presented to achieve indoor positioning with high accuracy and strong availability.…”

Section: Introductionmentioning

confidence: 99%

WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

Cao

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

A novel two-way ranging approach was introduced into the Wireless Fidelity (WiFi) standard, and its ranging accuracy reached one meter in a low multipath environment. However, in harsh environments due to multipath or non-line of sight (NLOS), the range measurement based on the WiFi round trip time (RTT) usually has low accuracy and cannot maintain the one-meter accuracy. Thus, this paper proposes an indoor positioning method based on Gaussian process regression (GPR) for harsh environments. There are two stages in the proposed method: construction of a positioning model and location estimation. In the model construction stage, based on known positions of access points (APs), we can determine the position coordinates of some ground points and the reference distances between them and the APs, and the offline ranging difference fingerprints can be generated by the reference distances, which means that there is no need to collect data. Gaussian process regression (GPR) utilizes offline ranging difference fingerprints based on the reference distance to establish a positioning model, and the particle swarm optimization (PSO) algorithm is employed to estimate the GPR hyperparameters. In the location estimation stage, the gathered actual range measurements generate the online ranging difference fingerprint, which is the input data of the positioning model. The output of the model is the estimated position of the smartphone. Experimental results show that the mean errors (MEs) of the proposed method and Least Squares (LS) algorithm are 1.097 and 3.484 meters, respectively, in a harsh environment, and the positioning accuracy of the proposed method improved by 68.5% compared with the LS algorithm.

show abstract

Technological Viability Assessment of Bluetooth Low Energy Technology for Indoor Localization

Cited by 33 publications

References 32 publications

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

An Outdoor Navigation Assistance System for Visually Impaired People in Public Transportation

WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

Contact Info

Product

Resources

About