Integrity Monitoring for Bluetooth Low Energy Beacons RSSI Based Indoor Positioning

Indoor wireless positioning using Bluetooth Low Energy (BLE) beacons have attracted considerable attention from industry and academia given the many advantages of this technology such as low power consumption, low cost, easy layout, high availability, and high precision. However, the indoor positioning accuracy always suffers from non-line of sight (NLOS) propagation, stemming from the frequently occurring instances of reflection, refraction, or scattering of BLE radio signals due to the complexity of indoor environments. This article proposes an integrity monitoring (IM) algorithm to detect and eliminate two gross errors simultaneously to solve the adverse effects caused by the NLOS. The logarithmic attenuation model based on the received signal strength indication (RSSI) of BLE realizes positioning by combining trilateration and Least Squares Based on the Taylor expansion (LSBT). Furthermore, the IM based on hypothesis testing is employed to improve the positioning quality andthe users will be alerted in time to avoid risk from positioning accuracy no longer meet user’s requirement. The performance of the proposed IM algorithm has been extensively tested by conducting simulation and field experiments. The experimental results show that the IM algorithm significantly improved BLE positioning accuracy as well as the robustness of the positioning system. The 90% average error (1.9143m) in seven groups of single point experiments was reduced by 34.48% over the 90% average error (2.9143m) of the LSBT method after performing IM, and the maximum error during continuous positioning did not exceed 3m after performing IM, which were better than only using LSBT positioning.

[1]  Ralf Vandenhouten,et al.  High-precision Optical Position Measurement in Indoor Environments , 2015 .

[2]  Honggui Li,et al.  Low-Cost 3D Bluetooth Indoor Positioning with Least Square , 2014, Wirel. Pers. Commun..

[3]  José Luis Lázaro,et al.  Coverage-Mapping Method Based on a Hardware Model for Mobile-Robot Positioning in Intelligent Spaces , 2010, IEEE Transactions on Instrumentation and Measurement.

[4]  Ozan K. Tonguz,et al.  Bluetooth 5: A Concrete Step Forward toward the IoT , 2017, IEEE Communications Magazine.

[5]  Mohammad Ali,et al.  An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT) , 2016, 2016 International Conference on Computer and Communication Engineering (ICCCE).

[6]  Valérie Renaudin,et al.  Magnetic field based heading estimation for pedestrian navigation environments , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[7]  Yuwei Chen,et al.  An Inquiry-based Bluetooth indoor positioning approach for the Finnish pavilion at Shanghai World Expo 2010 , 2010, IEEE/ION Position, Location and Navigation Symposium.

[8]  M.R. Mahfouz,et al.  Investigation of High-Accuracy Indoor 3-D Positioning Using UWB Technology , 2008, IEEE Transactions on Microwave Theory and Techniques.

[9]  Andy Hopper,et al.  Broadband ultrasonic location systems for improved indoor positioning , 2006, IEEE Transactions on Mobile Computing.

[10]  Olivier Julien,et al.  Analysis on the TOA Tracking With DVB-T Signals for Positioning , 2016, IEEE Transactions on Broadcasting.

[11]  Yue Liu,et al.  Bluetooth positioning using RSSI and triangulation methods , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[12]  Santiago Mazuelas,et al.  Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks , 2009, IEEE Journal of Selected Topics in Signal Processing.

[13]  Shih-Hau Fang,et al.  An Enhanced ZigBee Indoor Positioning System With an Ensemble Approach , 2012, IEEE Communications Letters.

[14]  M. Matsumoto,et al.  RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering , 2007 .

[15]  Robert Harle,et al.  Location Fingerprinting With Bluetooth Low Energy Beacons , 2015, IEEE Journal on Selected Areas in Communications.

[16]  D. Akopian,et al.  Validation of HDOP Measure for Impact Detection in Sensor Network-Based Structural Health Monitoring , 2009, IEEE Sensors Journal.

[17]  P. C. Deepesh,et al.  Experiences with using iBeacons for Indoor Positioning , 2016, ISEC.

[18]  Per Enge,et al.  Local area augmentation of GPS for the precision approach of aircraft , 1999, Proc. IEEE.

[19]  Jian Wang,et al.  A Bluetooth/PDR Integration Algorithm for an Indoor Positioning System , 2015, Sensors.

[20]  Yuwei Chen,et al.  Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints , 2013, Wirel. Pers. Commun..

[21]  Richard G. Mills User requirements , 1975, AFIPS '75.

[22]  Ruizhi Chen,et al.  An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras , 2018, Sensors.

[23]  Cheng Zhou,et al.  Bluetooth Indoor Positioning Based on RSSI and Kalman Filter , 2017, Wirel. Pers. Commun..

[24]  Mark A. Sturza,et al.  Navigation System Integrity Monitoring Using Redundant Measurements , 1988 .

[26]  Jun-Ho Huh,et al.  An Indoor Location-Based Control System Using Bluetooth Beacons for IoT Systems , 2017, Sensors.

[27]  Di Zhang,et al.  A Holistic Approach to Guarantee the Reliability of Positioning Based on Carrier Phase for Indoor Pseudolite , 2020, Applied Sciences.

[28]  Michael J. Rycroft,et al.  Understanding GPS. Principles and Applications , 1997 .

[29]  R. Grover Brown,et al.  A Baseline GPS RAIM Scheme and a Note on the Equivalence of Three RAIM Methods , 1992 .

[30]  Olivier Julien,et al.  TOA Estimation for Positioning With DVB-T Signals in Outdoor Static Tests , 2015, IEEE Transactions on Broadcasting.

[31]  M. Sankaran Approximations to the noncentral chi-square distribution , 1963 .

[32]  Ao Peng,et al.  Pedestrian Dead Reckoning-Assisted Visual Inertial Odometry Integrity Monitoring , 2019, Sensors.

[33]  Huai-Rong Shao,et al.  WiFi-based indoor positioning , 2015, IEEE Communications Magazine.

[34]  Jun Yan,et al.  Accurate DOA Estimation With Adjacent Angle Power Difference for Indoor Localization , 2020, IEEE Access.

[35]  Haiyong Luo,et al.  RSSI based Bluetooth low energy indoor positioning , 2014, IPIN.

[36]  Alexis Quesada-Arencibia,et al.  A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy , 2018, IEEE Access.

[37]  Naser El-Sheimy,et al.  Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons , 2016, Sensors.

[38]  C. L. Philip Chen,et al.  Integrity monitoring and thresholding-based WLAN indoor positioning algorithm for mobile devices , 2011, 2011 6th International Conference on System of Systems Engineering.

[39]  Mingquan Lu,et al.  GPS RAIM : Statistics Based Improvement on the Calculation of Threshold and Horizontal Protection Radius , 2005 .

[40]  Thomas Zwick,et al.  Integrity monitoring for UWB/INS tightly coupled pedestrian indoor scenarios , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[41]  David Akopian,et al.  Integrity monitoring in WLAN positioning systems , 2009, Defense + Commercial Sensing.

[42]  Josep Paradells Aspas,et al.  A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering , 2017, Sensors.

[43]  Lenan Wu,et al.  Mobile Tracking in Mixed Line-of-Sight/Non-Line-of-Sight Conditions: Algorithm and Theoretical Lower Bound , 2012, Wirel. Pers. Commun..

[44]  Chen Liang,et al.  Indoor Positioning with Smartphones:The State-of-the-art and the Challenges , 2017 .